{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcd1bf6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils\n",
    "import torch.utils.data\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from math import pi"
   ]
  },
  {
   "attachments": {
    "Formula_rnn.webp": {
     "image/webp": "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"
    }
   },
   "cell_type": "markdown",
   "id": "3b323ffe",
   "metadata": {},
   "source": [
    "![Formula_rnn.webp](attachment:Formula_rnn.webp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "736fdf10",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNCell(nn.Module):\n",
    "    \n",
    "    def __init__(self, inputSize, hiddenSize, outputSize):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.Wx = torch.randn(hiddenSize, inputSize) # input weights\n",
    "        self.Wh = torch.randn(hiddenSize, hiddenSize) # hidden weights\n",
    "        self.Wy = torch.randn(outputSize,hiddenSize) # output weights\n",
    "        self.h = torch.zeros(hiddenSize,1) # initial hidden state\n",
    "        self.bh = torch.zeros(hiddenSize,1) # hidden state bias\n",
    "        self.by = torch.zeros(outputSize,1) # output bias\n",
    "        \n",
    "    def forward(self, x):\n",
    "        self.h = torch.tanh(self.bh + torch.matmul(self.Wx, x) + torch.matmul(self.Wh,self.h))\n",
    "        output = nn.Softmax(self.by + torch.matmul(self.Wy,self.h))\n",
    "        \n",
    "        return output, self.h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34a546cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RNNCell(1, 20, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93c1cab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate input data\n",
    "x = np.linspace(0, 2 * np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x = torch.from_numpy(x).float().unsqueeze(1)\n",
    "y = torch.from_numpy(y).float().unsqueeze(1)\n",
    "\n",
    "# Define the RNN model\n",
    "class RNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(RNN, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.Wxh = nn.Linear(input_size, hidden_size)\n",
    "        self.Whh = nn.Linear(hidden_size, hidden_size)\n",
    "        self.Why = nn.Linear(hidden_size, output_size)\n",
    "        self.tanh = nn.Tanh()\n",
    "\n",
    "    def forward(self, x):\n",
    "        h_prev = torch.zeros(x.size(0), self.hidden_size)\n",
    "\n",
    "        for t in range(x.size(1)):\n",
    "            h_t = self.tanh(self.Wxh(x[:, t, :]) + self.Whh(h_prev))\n",
    "            h_prev = h_t\n",
    "\n",
    "        output = self.Why(h_t)\n",
    "        return output\n",
    "\n",
    "# Set hyperparameters\n",
    "input_size = 1\n",
    "hidden_size = 32\n",
    "output_size = 1\n",
    "\n",
    "# Create the RNN model\n",
    "model = RNN(input_size, hidden_size, output_size)\n",
    "\n",
    "# Compute the explicit formulas for the forward pass\n",
    "h_prev = torch.zeros(1, hidden_size)\n",
    "Wxh = model.Wxh.weight\n",
    "Whh = model.Whh.weight\n",
    "Why = model.Why.weight\n",
    "\n",
    "for t in range(x.size(1)):\n",
    "    x_t = x[:, t, :]\n",
    "    linear1 = torch.matmul(x_t, Wxh.t())\n",
    "    linear2 = torch.matmul(h_prev, Whh.t())\n",
    "    h_t = torch.tanh(linear1 + linear2)\n",
    "    h_prev = h_t\n",
    "\n",
    "output = torch.matmul(h_t, Why.t())\n",
    "\n",
    "# Compare the computed output with the model's output\n",
    "print(\"Computed output:\", output.item())\n",
    "model_output = model(x)\n",
    "print(\"Model output:\", model_output.item())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36074191",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e92f2e3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcad7708",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1919d2b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cab38237",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4060ca3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abea4b57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3119c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc6a746f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8366a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a084d2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 20 # Period of Sin Function\n",
    "L = 1000 # Sample Lenght of Sin function\n",
    "N = 200   # Sequence Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "087c4bbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.empty((N, L), 'int32')\n",
    "x[:] = np.array(range(L))\n",
    "data = np.sin(x * (1.0 / T)).astype('float32')\n",
    "x_f = np.array(range(1500))\n",
    "data_f = np.sin(x_f * (1.0 / T)).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "206176aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"x\", x.shape)\n",
    "print(\"x[:]\", x[:].shape)\n",
    "print(\"data\", data.shape)\n",
    "print(\"x_f\", x_f.shape)\n",
    "print(\"data_f\", data_f.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bec287c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8c508fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4659e354",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d574966",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a640ddb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f129a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = torch.from_numpy(data[1:, :-1])\n",
    "print(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a950a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = torch.from_numpy(data[:1, :-1])\n",
    "print(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4769a889",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_target = torch.from_numpy(data[1:, 1:])\n",
    "print(train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd3bde42",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_target = torch.from_numpy(data[:1, 1:])\n",
    "print(test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65f8b00f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNSin(nn.Module):\n",
    "    def __init__(self, in_dim, h_dim, out_dim):\n",
    "        super(RNNSin, self).__init__()\n",
    "        self.rnn = nn.RNNCell(in_dim, h_dim)\n",
    "        self.linear = nn.Linear(h_dim, out_dim)\n",
    "        self.h_dim = h_dim\n",
    "\n",
    "    def forward(self, train_data, future = 0):\n",
    "        outputs = []        \n",
    "        h_t = torch.zeros((train_data.size(0),self.h_dim))\n",
    "        for i, input_t in enumerate(train_data.chunk(train_data.size(1), dim=1)):\n",
    "            h_t = self.rnn(input_t,h_t)\n",
    "            output = self.linear(h_t)\n",
    "            outputs.append(output)\n",
    "        for i in range(future):            \n",
    "            h_t = self.rnn(outputs[-1],h_t)\n",
    "            output = self.linear(h_t)\n",
    "            outputs += [output]\n",
    "        outputs = torch.stack(outputs, 1).squeeze(2)\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6069af3",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq = RNNCell(1,200,1)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e42ef3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.MSELoss()\n",
    "optimizer = optim.LBFGS(seq.parameters(), lr=0.4)\n",
    "Nepochs = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "125b31d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(30,15))\n",
    "image_list = []\n",
    "for it in range(Nepochs):\n",
    "    \n",
    "    \n",
    "    def closure():\n",
    "        optimizer.zero_grad()\n",
    "        out = seq(train_data)\n",
    "        loss = loss_fn(out, train_target)\n",
    "        print('\\rEpoch:', it,'loss:', loss.item(), end='')\n",
    "        loss.backward()\n",
    "        return loss\n",
    "    optimizer.step(closure)\n",
    "    \n",
    "    future = 500\n",
    "    # begin to predict, no need to track gradient here\n",
    "    with torch.no_grad():\n",
    "        pred = seq(test_data, future=future)\n",
    "        loss = loss_fn(pred[:, :-future], test_target)\n",
    "        print(' test loss:', loss.item())\n",
    "        y = pred.detach().numpy()\n",
    "    plt.cla()\n",
    "    ax.set_xlabel('x', fontsize=26)\n",
    "    ax.set_ylabel('y', fontsize=26)\n",
    "    ax.set_xlim([0,1600])\n",
    "    ax.set_ylim([-1.5, 1.5])\n",
    "    ax.plot(x_f, data_f, \"-r\", linewidth = 3.0, label=\"Actual Series\")\n",
    "    ax.plot(np.arange(train_data.size(1)), y[0][:train_data.size(1)], '--k', linewidth = 2.0, label=\"Training\")\n",
    "    ax.plot(np.arange(train_data.size(1), train_data.size(1) + future), y[0][train_data.size(1):], 'b' + ':', linewidth = 3.0, label=\"Test + Future\")\n",
    "    ax.vlines(1000, -1.5, 1.5, colors=\"black\", linestyles='solid', linewidth=2.0)   \n",
    "    ax.text(500.0, 1.4, 'It = %d,' %it, fontdict={'size': 26, 'color':  'black'})\n",
    "    plt.legend(loc=2, prop={'size': 20})\n",
    "    fig.canvas.draw()\n",
    "    image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')\n",
    "    image  = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
    "    image_list.append(image)\n",
    "#imageio.mimsave('./RNN_sine_1.gif', image_list, duration=200)\n",
    "\n",
    "\n",
    "\n",
    "print(\"train_data\", train_data.shape)\n",
    "print(\"train_target\", train_target.shape)\n",
    "print(\"test_data\", test_data.shape)\n",
    "print(\"test_target\", test_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86b09459",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b13631b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceb58a88",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"test_target\", test_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d10308c",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 20 # Period of Sin Function\n",
    "L = 1000 # Sample Lenght of Sin function\n",
    "N = 200   # Sequence Length\n",
    "\n",
    "\n",
    "x = np.empty((N, L), 'int32')\n",
    "x[:] = np.array(range(L))\n",
    "data = np.sin(x * (1.0 / T)).astype('float32')\n",
    "x_f = np.array(range(1500))\n",
    "data_f = np.sin(x_f * (1.0 / T)).astype('float32')\n",
    "\n",
    "train_data = torch.from_numpy(data[1:, :-1])\n",
    "train_target = torch.from_numpy(data[1:, 1:])\n",
    "test_data = torch.from_numpy(data[:1, :-1])\n",
    "test_target = torch.from_numpy(data[:1, 1:])\n",
    "\n",
    "print(train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab4a7a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(train_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff73aa96",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d312474e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(test_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40dcfa34",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c369198",
   "metadata": {},
   "outputs": [],
   "source": [
    "x[:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d96cb39b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "155b13f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = np.array(range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c81b6fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aadfeef",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = seq(test_data, future=future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1f33ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "k.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ec7526",
   "metadata": {},
   "outputs": [],
   "source": [
    "future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f29ac08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate training data\n",
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x_train = torch.Tensor(x).view(-1, 1, 1)\n",
    "y_train = torch.Tensor(y).view(-1, 1)\n",
    "\n",
    "# Define the RNNCell model\n",
    "class RNNCell(nn.Module):\n",
    "    def __init__(self, inputSize, hiddenSize, outputSize):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.Wx = nn.Parameter(torch.randn(hiddenSize, inputSize))  # input weights\n",
    "        self.Wh = nn.Parameter(torch.randn(hiddenSize, hiddenSize))  # hidden weights\n",
    "        self.Wy = nn.Parameter(torch.randn(outputSize, hiddenSize))  # output weights\n",
    "        self.bh = nn.Parameter(torch.zeros(hiddenSize, 1))  # hidden state bias\n",
    "        self.by = nn.Parameter(torch.zeros(outputSize, 1))  # output bias\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        seq_len = x.size(1)\n",
    "\n",
    "        h = torch.zeros(batch_size, self.Wh.size(0), 1)  # initial hidden state\n",
    "\n",
    "        outputs = []\n",
    "        for t in range(seq_len):\n",
    "            h = torch.tanh(self.bh + torch.matmul(self.Wx, x[:, t, :].unsqueeze(2)) + torch.matmul(self.Wh, h))\n",
    "            output = nn.Softmax(dim=1)(self.by + torch.matmul(self.Wy, h))\n",
    "            outputs.append(output)\n",
    "\n",
    "        return torch.stack(outputs, dim=1), h\n",
    "\n",
    "# Set hyperparameters\n",
    "inputSize = 1\n",
    "hiddenSize = 200\n",
    "outputSize = 1\n",
    "num_epochs = 1000\n",
    "learning_rate = 0.001\n",
    "\n",
    "# Initialize the RNNCell model\n",
    "model = RNNCell(inputSize, hiddenSize, outputSize)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    outputs, _ = model(x_train)\n",
    "    loss = criterion(outputs[:, -1, :], y_train)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}\")\n",
    "\n",
    "# Test the model\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    x_test = torch.Tensor(np.linspace(0, 2*np.pi, 1000)).view(-1, 1, 1)\n",
    "    y_pred, _ = model(x_test)\n",
    "    y_pred = y_pred[:, -1, :].numpy().flatten()\n",
    "\n",
    "# Plot the results\n",
    "plt.plot(x, y, label='sin(x)')\n",
    "plt.plot(x_test.numpy().flatten(), y_pred, label='RNNCell Approximation')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "947be67d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate training data\n",
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Normalize the input data\n",
    "x_mean, x_std = np.mean(x), np.std(x)\n",
    "y_mean, y_std = np.mean(y), np.std(y)\n",
    "x_normalized = (x - x_mean) / x_std\n",
    "y_normalized = (y - y_mean) / y_std\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x_train = torch.Tensor(x_normalized).view(-1, 1, 1)\n",
    "y_train = torch.Tensor(y_normalized).view(-1, 1)\n",
    "\n",
    "# Define the RNNCell model\n",
    "class RNNCell(nn.Module):\n",
    "    def __init__(self, inputSize, hiddenSize, outputSize):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.Wx = nn.Parameter(torch.randn(hiddenSize, inputSize))  # input weights\n",
    "        self.Wh = nn.Parameter(torch.randn(hiddenSize, hiddenSize))  # hidden weights\n",
    "        self.Wy = nn.Parameter(torch.randn(outputSize, hiddenSize))  # output weights\n",
    "        self.bh = nn.Parameter(torch.zeros(hiddenSize, 1))  # hidden state bias\n",
    "        self.by = nn.Parameter(torch.zeros(outputSize, 1))  # output bias\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        seq_len = x.size(1)\n",
    "\n",
    "        h = torch.zeros(batch_size, self.Wh.size(0), 1)  # initial hidden state\n",
    "\n",
    "        outputs = []\n",
    "        for t in range(seq_len):\n",
    "            h = torch.tanh(self.bh + torch.matmul(self.Wx, x[:, t, :].unsqueeze(2)) + torch.matmul(self.Wh, h))\n",
    "            output = nn.Softmax(dim=1)(self.by + torch.matmul(self.Wy, h))\n",
    "            outputs.append(output)\n",
    "\n",
    "        return torch.stack(outputs, dim=1), h\n",
    "\n",
    "# Set hyperparameters\n",
    "inputSize = 1\n",
    "hiddenSize = 64\n",
    "outputSize = 1\n",
    "num_epochs = 1000\n",
    "learning_rate = 0.001\n",
    "\n",
    "# Initialize the RNNCell model\n",
    "model = RNNCell(inputSize, hiddenSize, outputSize)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    outputs, _ = model(x_train)\n",
    "    loss = criterion(outputs[:, -1, :], y_train)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch + 1) % 100 == 0:\n",
    "        print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}\")\n",
    "\n",
    "# Test the model\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    x_test = torch.Tensor(np.linspace(0, 2*np.pi, 1000)).view(-1, 1, 1)\n",
    "    y_pred, _ = model(x_test)\n",
    "    y_pred = (y_pred[:, -1, :] * y_std) + y_mean\n",
    "    y_pred = y_pred.numpy().flatten()\n",
    "\n",
    "# Plot the results\n",
    "plt.plot(x, y, label='sin(x)')\n",
    "plt.plot(x_test.numpy().flatten(), y_pred, label='RNNCell Approximation')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56d3c8ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate training data\n",
    "x = np.linspace(0, 2 * np.pi, 1000)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Normalize the input data\n",
    "x_mean, x_std = np.mean(x), np.std(x)\n",
    "y_mean, y_std = np.mean(y), np.std(y)\n",
    "x_normalized = (x - x_mean) / x_std\n",
    "y_normalized = (y - y_mean) / y_std\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x_train = torch.Tensor(x_normalized).view(-1, 1, 1)\n",
    "y_train = torch.Tensor(y_normalized).view(-1, 1)\n",
    "\n",
    "# Define the RNNCell model\n",
    "class RNNCell(nn.Module):\n",
    "    def __init__(self, inputSize, hiddenSize, outputSize):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.hiddenSize = hiddenSize\n",
    "        self.rnn = nn.RNN(inputSize, hiddenSize, batch_first=True)\n",
    "        self.fc = nn.Linear(hiddenSize, outputSize)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out, _ = self.rnn(x)\n",
    "        out = self.fc(out[:, -1, :])  # Use the last output only\n",
    "        return out\n",
    "\n",
    "# Set hyperparameters\n",
    "inputSize = 1\n",
    "hiddenSize = 200\n",
    "outputSize = 1\n",
    "num_epochs = 10000\n",
    "learning_rate = 0.001\n",
    "\n",
    "# Initialize the RNNCell model\n",
    "model = RNNCell(inputSize, hiddenSize, outputSize)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    outputs = model(x_train)\n",
    "    loss = criterion(outputs, y_train)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch + 1) % 100 == 0:\n",
    "        print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}\")\n",
    "\n",
    "# Test the model\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    x_test = torch.Tensor(np.linspace(0, 2*np.pi, 1000)).view(-1, 1, 1)\n",
    "    y_pred = model(x_test).numpy().flatten()\n",
    "\n",
    "# Scale the predictions back to the original range\n",
    "y_pred = (y_pred * y_std) + y_mean\n",
    "\n",
    "# Plot the results\n",
    "plt.plot(x, y, label='sin(x)')\n",
    "plt.plot(x, y_pred, label='RNNCell Approximation')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa0cbb29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate training data\n",
    "x = np.linspace(0, 2 * np.pi, 1000)\n",
    "y = np.sin(x)\n",
    "\n",
    "# # Normalize the input data\n",
    "# x_mean, x_std = np.mean(x), np.std(x)\n",
    "# y_mean, y_std = np.mean(y), np.std(y)\n",
    "# x_normalized = (x - x_mean) / x_std\n",
    "# y_normalized = (y - y_mean) / y_std\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x_train = torch.Tensor(x).view(-1, 1, 1)\n",
    "y_train = torch.Tensor(y).view(-1, 1)\n",
    "\n",
    "# Define the RNNCell model\n",
    "class RNNCell(nn.Module):\n",
    "    def __init__(self, inputSize, hiddenSize, outputSize):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.Wx = nn.Parameter(torch.randn(hiddenSize, inputSize))  # input weights\n",
    "        self.Wh = nn.Parameter(torch.randn(hiddenSize, hiddenSize))  # hidden weights\n",
    "        self.Wy = nn.Parameter(torch.randn(outputSize, hiddenSize))  # output weights\n",
    "        self.bh = nn.Parameter(torch.zeros(hiddenSize, 1))  # hidden state bias\n",
    "        self.by = nn.Parameter(torch.zeros(outputSize, 1))  # output bias\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        seq_len = x.size(1)\n",
    "\n",
    "        h = torch.zeros(batch_size, self.Wh.size(0), 1)  # initial hidden state\n",
    "\n",
    "        outputs = []\n",
    "        for t in range(seq_len):\n",
    "            h = torch.tanh(self.bh + torch.matmul(self.Wx, x[:, t, :].unsqueeze(2)) + torch.matmul(self.Wh, h))\n",
    "            output = nn.Softmax(dim=1)(self.by + torch.matmul(self.Wy, h))\n",
    "            outputs.append(output)\n",
    "\n",
    "        return torch.stack(outputs, dim=1), h\n",
    "\n",
    "# Set hyperparameters\n",
    "inputSize = 1\n",
    "hiddenSize = 20\n",
    "outputSize = 1\n",
    "num_epochs = 1000\n",
    "learning_rate = 0.1\n",
    "\n",
    "# Initialize the RNNCell model\n",
    "model = RNNCell(inputSize, hiddenSize, outputSize)\n",
    "\n",
    "# Define the loss function\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "# Define the closure for LBFGS\n",
    "def closure():\n",
    "    optimizer.zero_grad()\n",
    "    outputs, _ = model(x_train)\n",
    "    loss = criterion(outputs[:, -1, :], y_train)\n",
    "    loss.backward()\n",
    "    return loss\n",
    "\n",
    "# Initialize the LBFGS optimizer\n",
    "optimizer = optim.LBFGS(model.parameters(), lr=learning_rate, max_iter=2000)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    optimizer.step(closure)\n",
    "\n",
    "    if (epoch + 1) % 100 == 0:\n",
    "        with torch.no_grad():\n",
    "            outputs, _ = model(x_train)\n",
    "            loss = criterion(outputs[:, -1, :], y_train)\n",
    "            print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}\")\n",
    "\n",
    "# Test the model\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    x_test = torch.Tensor(np.linspace(0, 2 * np.pi, 1000)).view(-1, 1, 1)\n",
    "    y_pred, _ = model(x_test)\n",
    "#     y_pred = (y_pred[:, -1, :] * y_std) + y_mean\n",
    "#     y_pred = y_pred.numpy().flatten()\n",
    "\n",
    "# # Scale the predictions back to the original range\n",
    "# y_pred = (y_pred * y_std) + y_mean\n",
    "\n",
    "# Plot the results\n",
    "plt.plot(x, y, label='sin(x)')\n",
    "plt.plot(x_test.numpy().flatten(), y_pred, label='RNNCell Approximation')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e79fb28d",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 7th July|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4754e78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5182c3dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate input data\n",
    "x = np.linspace(0, 2 * np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x = torch.from_numpy(x).unsqueeze(1).float()\n",
    "y = torch.from_numpy(y).unsqueeze(1).float()\n",
    "\n",
    "# Define the RNN cell\n",
    "class RNNCell(nn.Module):\n",
    "    def __init__(self, inputSize, hiddenSize, outputSize):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.Wx = nn.Parameter(torch.randn(hiddenSize, inputSize))  # input weights\n",
    "        self.Wh = nn.Parameter(torch.randn(hiddenSize, hiddenSize))  # hidden weights\n",
    "        self.Wy = nn.Parameter(torch.randn(outputSize, hiddenSize))  # output weights\n",
    "        self.h = torch.zeros(hiddenSize, 1)  # initial hidden state\n",
    "        self.bh = nn.Parameter(torch.zeros(hiddenSize, 1))  # hidden state bias\n",
    "        self.by = nn.Parameter(torch.zeros(outputSize, 1))  # output bias\n",
    "\n",
    "    def forward(self, x):\n",
    "        outputs = []\n",
    "        for t in range(x.size(0)):\n",
    "            self.h = torch.tanh(self.bh + torch.matmul(self.Wx, x[t]) + torch.matmul(self.Wh, self.h))\n",
    "            output = nn.functional.softmax(self.by + torch.matmul(self.Wy, self.h), dim=0)\n",
    "            outputs.append(output)\n",
    "        return torch.stack(outputs, dim=0), self.h\n",
    "\n",
    "# Set hyperparameters\n",
    "inputSize = 1\n",
    "hiddenSize = 32\n",
    "outputSize = 1\n",
    "learningRate = 0.01\n",
    "numEpochs = 100\n",
    "\n",
    "# Create the RNN model\n",
    "model = RNNCell(inputSize, hiddenSize, outputSize)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learningRate)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(numEpochs):\n",
    "    model.train()\n",
    "    outputs, _ = model(x)\n",
    "    loss = criterion(outputs, y)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward(retain_graph=True)\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f\"Epoch: {epoch+1}/{numEpochs}, Loss: {loss.item()}\")\n",
    "\n",
    "# Predict with the trained model\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    predictions, _ = model(x)\n",
    "\n",
    "# Print some predictions\n",
    "for i in range(10):\n",
    "    print(\"Input:\", x[i].item(), \"True output:\", y[i].item(), \"Predicted output:\", predictions[i].item())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cff8311",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate input data\n",
    "x = np.linspace(0, 2 * np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x = torch.from_numpy(x).unsqueeze(1).float()\n",
    "y = torch.from_numpy(y).unsqueeze(1).float()\n",
    "\n",
    "# Define the RNN cell\n",
    "class RNNCell(nn.Module):\n",
    "    def __init__(self, inputSize, hiddenSize, outputSize):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.Wx = nn.Parameter(torch.randn(hiddenSize, inputSize))  # input weights\n",
    "        self.Wh = nn.Parameter(torch.randn(hiddenSize, hiddenSize))  # hidden weights\n",
    "        self.Wy = nn.Parameter(torch.randn(outputSize, hiddenSize))  # output weights\n",
    "        self.h = torch.zeros(hiddenSize, 1)  # initial hidden state  # initial hidden state\n",
    "        self.bh = nn.Parameter(torch.zeros(hiddenSize, 1))  # hidden state bias\n",
    "        self.by = nn.Parameter(torch.zeros(outputSize, 1))  # output bias\n",
    "\n",
    "    def forward(self, x):\n",
    "        outputs = []\n",
    "        for t in range(x.size(0)):\n",
    "            self.h = torch.tanh(self.bh + torch.matmul(self.Wx, x[t]) + torch.matmul(self.Wh, self.h))\n",
    "            output = nn.functional.softmax(self.by + torch.matmul(self.Wy, self.h), dim=0)\n",
    "            outputs.append(output)\n",
    "        return torch.stack(outputs, dim=0), self.h\n",
    "\n",
    "# Set hyperparameters\n",
    "inputSize = 1\n",
    "hiddenSize = 32\n",
    "outputSize = 1\n",
    "learningRate = 0.01\n",
    "numEpochs = 100\n",
    "\n",
    "# Create the RNN model\n",
    "model = RNNCell(inputSize, hiddenSize, outputSize)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learningRate)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(numEpochs):\n",
    "    model.train()\n",
    "    outputs, _ = model(x)\n",
    "    loss = criterion(outputs, y)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward(retain_graph=True)\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f\"Epoch: {epoch+1}/{numEpochs}, Loss: {loss.item()}\")\n",
    "\n",
    "# Predict with the trained model\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    predictions, _ = model(x)\n",
    "\n",
    "# Print some predictions\n",
    "for i in range(10):\n",
    "    print(\"Input:\", x[i].item(), \"True output:\", y[i].item(), \"Predicted output:\", predictions[i].item())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddaa95cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate input data\n",
    "x = np.linspace(0, 2 * np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x = torch.from_numpy(x).unsqueeze(1).float()\n",
    "y = torch.from_numpy(y).unsqueeze(1).float()\n",
    "\n",
    "# Define the RNN cell\n",
    "class RNNCell(nn.Module):\n",
    "    def __init__(self, inputSize, hiddenSize, outputSize):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.Wx = nn.Parameter(torch.randn(hiddenSize, inputSize))  # input weights\n",
    "        self.Wh = nn.Parameter(torch.randn(hiddenSize, hiddenSize))  # hidden weights\n",
    "        self.Wy = nn.Parameter(torch.randn(outputSize, hiddenSize))  # output weights\n",
    "        self.h = torch.zeros(hiddenSize, 1)  # initial hidden state\n",
    "        self.bh = nn.Parameter(torch.zeros(hiddenSize, 1))  # hidden state bias\n",
    "        self.by = nn.Parameter(torch.zeros(outputSize, 1))  # output bias\n",
    "\n",
    "    def forward(self, x):\n",
    "        outputs = []\n",
    "        for t in range(x.size(0)):\n",
    "            h_tilde = torch.tanh(self.bh + torch.matmul(self.Wx, x[t]) + torch.matmul(self.Wh, self.h))\n",
    "            self.h = h_tilde.clone()\n",
    "            output = nn.functional.softmax(self.by + torch.matmul(self.Wy, h_tilde), dim=0)\n",
    "            outputs.append(output)\n",
    "        return torch.stack(outputs, dim=0), self.h\n",
    "\n",
    "# Set hyperparameters\n",
    "inputSize = 1\n",
    "hiddenSize = 32\n",
    "outputSize = 1\n",
    "learningRate = 0.01\n",
    "numEpochs = 100\n",
    "\n",
    "# Create the RNN model\n",
    "model = RNNCell(inputSize, hiddenSize, outputSize)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learningRate)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(numEpochs):\n",
    "    model.train()\n",
    "    outputs, _ = model(x)\n",
    "    loss = criterion(outputs, y)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward(retain_graph=True)\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f\"Epoch: {epoch+1}/{numEpochs}, Loss: {loss.item()}\")\n",
    "\n",
    "# Predict with the trained model\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    predictions, _ = model(x)\n",
    "\n",
    "# Print some predictions\n",
    "for i in range(10):\n",
    "    print(\"Input:\", x[i].item(), \"True output:\", y[i].item(), \"Predicted output:\", predictions[i].item())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7e08943",
   "metadata": {},
   "outputs": [],
   "source": [
    "# trying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cae5738",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "# Define the RNN class\n",
    "class RNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(RNN, self).__init__()\n",
    "\n",
    "        self.hidden_size = hidden_size\n",
    "        self.Wxh = nn.Linear(input_size, hidden_size)\n",
    "        self.Whh = nn.Linear(hidden_size, hidden_size)\n",
    "        self.Why = nn.Linear(hidden_size, output_size)\n",
    "        self.activation = nn.Tanh()\n",
    "\n",
    "    def forward(self, input):\n",
    "        seq_len = input.size(0)\n",
    "        batch_size = input.size(1)\n",
    "        hidden_state = torch.zeros(batch_size, self.hidden_size)\n",
    "\n",
    "        output_seq = []\n",
    "        for i in range(seq_len):\n",
    "            hidden_state = self.activation(self.Wxh(input[i]) + self.Whh(hidden_state))\n",
    "            output = self.Why(hidden_state)\n",
    "            output_seq.append(output)\n",
    "\n",
    "        return torch.stack(output_seq)\n",
    "\n",
    "# Hyperparameters\n",
    "input_size = 1\n",
    "hidden_size = 200\n",
    "output_size = 1\n",
    "seq_len = 200\n",
    "learning_rate = 0.01\n",
    "num_epochs = 10000\n",
    "\n",
    "# Generate training data\n",
    "train_data = torch.sin(torch.linspace(0,  3.1416, seq_len + 1))[:-1].unsqueeze(1)\n",
    "target_data = torch.sin(torch.linspace(0, 3.1416, seq_len + 1))[1:].unsqueeze(1)\n",
    "\n",
    "# Initialize the RNN\n",
    "rnn = RNN(input_size, hidden_size, output_size)\n",
    "\n",
    "# Loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass\n",
    "    output_seq = rnn(train_data)\n",
    "    loss = criterion(output_seq, target_data)\n",
    "\n",
    "    # Backward and optimize\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch + 1) % 100 == 0:\n",
    "        print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}\")\n",
    "\n",
    "# Test the trained model\n",
    "test_data = torch.sin(torch.linspace(0,  3.1416, 200)).unsqueeze(1)\n",
    "with torch.no_grad():\n",
    "    rnn.eval()\n",
    "    predicted_seq = rnn(test_data)\n",
    "\n",
    "# Plot the results\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(test_data.squeeze(), label='Ground Truth')\n",
    "plt.plot(predicted_seq.squeeze().numpy(), label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a24eb8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# trying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fefb1a44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4a703ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# Define the RNN model\n",
    "class RNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_classes):\n",
    "        super(RNN, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, num_classes)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)\n",
    "        out, _ = self.rnn(x, h0)\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out\n",
    "\n",
    "# Set the device\n",
    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device = torch.device(\"cpu\")\n",
    "# Hyperparameters\n",
    "input_size = 28\n",
    "sequence_length = 28\n",
    "hidden_size = 128\n",
    "num_classes = 10\n",
    "num_epochs = 5\n",
    "batch_size = 100\n",
    "learning_rate = 0.001\n",
    "\n",
    "# Load the MNIST dataset\n",
    "train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)\n",
    "test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# Initialize the RNN model\n",
    "model = RNN(input_size, hidden_size, num_classes).to(device)\n",
    "\n",
    "# Loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training loop\n",
    "total_step = len(train_loader)\n",
    "for epoch in range(num_epochs):\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        images = images.view(-1, sequence_length, input_size).to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if (i + 1) % 100 == 0:\n",
    "            print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{total_step}], Loss: {loss.item():.4f}\")\n",
    "\n",
    "# Test the model\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for images, labels in test_loader:\n",
    "        images = images.view(-1, sequence_length, input_size).to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "    \n",
    "    print(f\"Test Accuracy: {(100 * correct / total):.2f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "672ceb4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1ebf16d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sinx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9a4b067",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/localhome/tkapoor/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([1000, 1, 1])) that is different to the input size (torch.Size([1000, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/5000], Loss: 0.5363\n",
      "Epoch [20/5000], Loss: 0.4998\n",
      "Epoch [30/5000], Loss: 0.5016\n",
      "Epoch [40/5000], Loss: 0.5005\n",
      "Epoch [50/5000], Loss: 0.4999\n",
      "Epoch [60/5000], Loss: 0.4996\n",
      "Epoch [70/5000], Loss: 0.4995\n",
      "Epoch [80/5000], Loss: 0.4995\n",
      "Epoch [90/5000], Loss: 0.4995\n",
      "Epoch [100/5000], Loss: 0.4995\n",
      "Epoch [110/5000], Loss: 0.4995\n",
      "Epoch [120/5000], Loss: 0.4995\n",
      "Epoch [130/5000], Loss: 0.4995\n",
      "Epoch [140/5000], Loss: 0.4995\n",
      "Epoch [150/5000], Loss: 0.4995\n",
      "Epoch [160/5000], Loss: 0.4995\n",
      "Epoch [170/5000], Loss: 0.4995\n",
      "Epoch [180/5000], Loss: 0.4995\n",
      "Epoch [190/5000], Loss: 0.4995\n",
      "Epoch [200/5000], Loss: 0.4995\n",
      "Epoch [210/5000], Loss: 0.4995\n",
      "Epoch [220/5000], Loss: 0.4995\n",
      "Epoch [230/5000], Loss: 0.4995\n",
      "Epoch [240/5000], Loss: 0.4995\n",
      "Epoch [250/5000], Loss: 0.4995\n",
      "Epoch [260/5000], Loss: 0.4995\n",
      "Epoch [270/5000], Loss: 0.4995\n",
      "Epoch [280/5000], Loss: 0.4995\n",
      "Epoch [290/5000], Loss: 0.4995\n",
      "Epoch [300/5000], Loss: 0.4995\n",
      "Epoch [310/5000], Loss: 0.4995\n",
      "Epoch [320/5000], Loss: 0.4995\n",
      "Epoch [330/5000], Loss: 0.4995\n",
      "Epoch [340/5000], Loss: 0.4995\n",
      "Epoch [350/5000], Loss: 0.4995\n",
      "Epoch [360/5000], Loss: 0.4995\n",
      "Epoch [370/5000], Loss: 0.4995\n",
      "Epoch [380/5000], Loss: 0.4995\n",
      "Epoch [390/5000], Loss: 0.4995\n",
      "Epoch [400/5000], Loss: 0.4995\n",
      "Epoch [410/5000], Loss: 0.4995\n",
      "Epoch [420/5000], Loss: 0.4995\n",
      "Epoch [430/5000], Loss: 0.4995\n",
      "Epoch [440/5000], Loss: 0.4995\n",
      "Epoch [450/5000], Loss: 0.4995\n",
      "Epoch [460/5000], Loss: 0.4995\n",
      "Epoch [470/5000], Loss: 0.4995\n",
      "Epoch [480/5000], Loss: 0.4995\n",
      "Epoch [490/5000], Loss: 0.4995\n",
      "Epoch [500/5000], Loss: 0.4995\n",
      "Epoch [510/5000], Loss: 0.4995\n",
      "Epoch [520/5000], Loss: 0.4995\n",
      "Epoch [530/5000], Loss: 0.4995\n",
      "Epoch [540/5000], Loss: 0.4995\n",
      "Epoch [550/5000], Loss: 0.4995\n",
      "Epoch [560/5000], Loss: 0.4995\n",
      "Epoch [570/5000], Loss: 0.4995\n",
      "Epoch [580/5000], Loss: 0.4995\n",
      "Epoch [590/5000], Loss: 0.4995\n",
      "Epoch [600/5000], Loss: 0.4995\n",
      "Epoch [610/5000], Loss: 0.4995\n",
      "Epoch [620/5000], Loss: 0.4995\n",
      "Epoch [630/5000], Loss: 0.4995\n",
      "Epoch [640/5000], Loss: 0.4995\n",
      "Epoch [650/5000], Loss: 0.4995\n",
      "Epoch [660/5000], Loss: 0.4995\n",
      "Epoch [670/5000], Loss: 0.4995\n",
      "Epoch [680/5000], Loss: 0.4995\n",
      "Epoch [690/5000], Loss: 0.4995\n",
      "Epoch [700/5000], Loss: 0.4995\n",
      "Epoch [710/5000], Loss: 0.4995\n",
      "Epoch [720/5000], Loss: 0.4995\n",
      "Epoch [730/5000], Loss: 0.4995\n",
      "Epoch [740/5000], Loss: 0.4995\n",
      "Epoch [750/5000], Loss: 0.4995\n",
      "Epoch [760/5000], Loss: 0.4995\n",
      "Epoch [770/5000], Loss: 0.4995\n",
      "Epoch [780/5000], Loss: 0.4995\n",
      "Epoch [790/5000], Loss: 0.4995\n",
      "Epoch [800/5000], Loss: 0.4995\n",
      "Epoch [810/5000], Loss: 0.4995\n",
      "Epoch [820/5000], Loss: 0.4995\n",
      "Epoch [830/5000], Loss: 0.4995\n",
      "Epoch [840/5000], Loss: 0.4995\n",
      "Epoch [850/5000], Loss: 0.4995\n",
      "Epoch [860/5000], Loss: 0.4995\n",
      "Epoch [870/5000], Loss: 0.4995\n",
      "Epoch [880/5000], Loss: 0.4995\n",
      "Epoch [890/5000], Loss: 0.4995\n",
      "Epoch [900/5000], Loss: 0.4995\n",
      "Epoch [910/5000], Loss: 0.4995\n",
      "Epoch [920/5000], Loss: 0.4995\n",
      "Epoch [930/5000], Loss: 0.4995\n",
      "Epoch [940/5000], Loss: 0.4995\n",
      "Epoch [950/5000], Loss: 0.4995\n",
      "Epoch [960/5000], Loss: 0.4995\n",
      "Epoch [970/5000], Loss: 0.4995\n",
      "Epoch [980/5000], Loss: 0.4995\n",
      "Epoch [990/5000], Loss: 0.4995\n",
      "Epoch [1000/5000], Loss: 0.4995\n",
      "Epoch [1010/5000], Loss: 0.4995\n",
      "Epoch [1020/5000], Loss: 0.4995\n",
      "Epoch [1030/5000], Loss: 0.4995\n",
      "Epoch [1040/5000], Loss: 0.4995\n",
      "Epoch [1050/5000], Loss: 0.4995\n",
      "Epoch [1060/5000], Loss: 0.4995\n",
      "Epoch [1070/5000], Loss: 0.4995\n",
      "Epoch [1080/5000], Loss: 0.4995\n",
      "Epoch [1090/5000], Loss: 0.4995\n",
      "Epoch [1100/5000], Loss: 0.4995\n",
      "Epoch [1110/5000], Loss: 0.4995\n",
      "Epoch [1120/5000], Loss: 0.4995\n",
      "Epoch [1130/5000], Loss: 0.4995\n",
      "Epoch [1140/5000], Loss: 0.4995\n",
      "Epoch [1150/5000], Loss: 0.4995\n",
      "Epoch [1160/5000], Loss: 0.4995\n",
      "Epoch [1170/5000], Loss: 0.4995\n",
      "Epoch [1180/5000], Loss: 0.4995\n",
      "Epoch [1190/5000], Loss: 0.4995\n",
      "Epoch [1200/5000], Loss: 0.4995\n",
      "Epoch [1210/5000], Loss: 0.4995\n",
      "Epoch [1220/5000], Loss: 0.4995\n",
      "Epoch [1230/5000], Loss: 0.4995\n",
      "Epoch [1240/5000], Loss: 0.4995\n",
      "Epoch [1250/5000], Loss: 0.4995\n",
      "Epoch [1260/5000], Loss: 0.4995\n",
      "Epoch [1270/5000], Loss: 0.4995\n",
      "Epoch [1280/5000], Loss: 0.4995\n",
      "Epoch [1290/5000], Loss: 0.4995\n",
      "Epoch [1300/5000], Loss: 0.4995\n",
      "Epoch [1310/5000], Loss: 0.4995\n",
      "Epoch [1320/5000], Loss: 0.4995\n",
      "Epoch [1330/5000], Loss: 0.4995\n",
      "Epoch [1340/5000], Loss: 0.4995\n",
      "Epoch [1350/5000], Loss: 0.4995\n",
      "Epoch [1360/5000], Loss: 0.4995\n",
      "Epoch [1370/5000], Loss: 0.4995\n",
      "Epoch [1380/5000], Loss: 0.4995\n",
      "Epoch [1390/5000], Loss: 0.4995\n",
      "Epoch [1400/5000], Loss: 0.4995\n",
      "Epoch [1410/5000], Loss: 0.4995\n",
      "Epoch [1420/5000], Loss: 0.4995\n",
      "Epoch [1430/5000], Loss: 0.4995\n",
      "Epoch [1440/5000], Loss: 0.4995\n",
      "Epoch [1450/5000], Loss: 0.4995\n",
      "Epoch [1460/5000], Loss: 0.4995\n",
      "Epoch [1470/5000], Loss: 0.4995\n",
      "Epoch [1480/5000], Loss: 0.4995\n",
      "Epoch [1490/5000], Loss: 0.4995\n",
      "Epoch [1500/5000], Loss: 0.4995\n",
      "Epoch [1510/5000], Loss: 0.4995\n",
      "Epoch [1520/5000], Loss: 0.4995\n",
      "Epoch [1530/5000], Loss: 0.4995\n",
      "Epoch [1540/5000], Loss: 0.4995\n",
      "Epoch [1550/5000], Loss: 0.4995\n",
      "Epoch [1560/5000], Loss: 0.4995\n",
      "Epoch [1570/5000], Loss: 0.4995\n",
      "Epoch [1580/5000], Loss: 0.4995\n",
      "Epoch [1590/5000], Loss: 0.4995\n",
      "Epoch [1600/5000], Loss: 0.4995\n",
      "Epoch [1610/5000], Loss: 0.4995\n",
      "Epoch [1620/5000], Loss: 0.4995\n",
      "Epoch [1630/5000], Loss: 0.4995\n",
      "Epoch [1640/5000], Loss: 0.4995\n",
      "Epoch [1650/5000], Loss: 0.4995\n",
      "Epoch [1660/5000], Loss: 0.4995\n",
      "Epoch [1670/5000], Loss: 0.4995\n",
      "Epoch [1680/5000], Loss: 0.4995\n",
      "Epoch [1690/5000], Loss: 0.4995\n",
      "Epoch [1700/5000], Loss: 0.4995\n",
      "Epoch [1710/5000], Loss: 0.4995\n",
      "Epoch [1720/5000], Loss: 0.4995\n",
      "Epoch [1730/5000], Loss: 0.4995\n",
      "Epoch [1740/5000], Loss: 0.4995\n",
      "Epoch [1750/5000], Loss: 0.4995\n",
      "Epoch [1760/5000], Loss: 0.4995\n",
      "Epoch [1770/5000], Loss: 0.4995\n",
      "Epoch [1780/5000], Loss: 0.4995\n",
      "Epoch [1790/5000], Loss: 0.4995\n",
      "Epoch [1800/5000], Loss: 0.4995\n",
      "Epoch [1810/5000], Loss: 0.4995\n",
      "Epoch [1820/5000], Loss: 0.4995\n",
      "Epoch [1830/5000], Loss: 0.4995\n",
      "Epoch [1840/5000], Loss: 0.4995\n",
      "Epoch [1850/5000], Loss: 0.4995\n",
      "Epoch [1860/5000], Loss: 0.4995\n",
      "Epoch [1870/5000], Loss: 0.4995\n",
      "Epoch [1880/5000], Loss: 0.4995\n",
      "Epoch [1890/5000], Loss: 0.4995\n",
      "Epoch [1900/5000], Loss: 0.4995\n",
      "Epoch [1910/5000], Loss: 0.4995\n",
      "Epoch [1920/5000], Loss: 0.4995\n",
      "Epoch [1930/5000], Loss: 0.4995\n",
      "Epoch [1940/5000], Loss: 0.4995\n",
      "Epoch [1950/5000], Loss: 0.4995\n",
      "Epoch [1960/5000], Loss: 0.4995\n",
      "Epoch [1970/5000], Loss: 0.4995\n",
      "Epoch [1980/5000], Loss: 0.4995\n",
      "Epoch [1990/5000], Loss: 0.4995\n",
      "Epoch [2000/5000], Loss: 0.4995\n",
      "Epoch [2010/5000], Loss: 0.4995\n",
      "Epoch [2020/5000], Loss: 0.4995\n",
      "Epoch [2030/5000], Loss: 0.4995\n",
      "Epoch [2040/5000], Loss: 0.4995\n",
      "Epoch [2050/5000], Loss: 0.4995\n",
      "Epoch [2060/5000], Loss: 0.4995\n",
      "Epoch [2070/5000], Loss: 0.4995\n",
      "Epoch [2080/5000], Loss: 0.4995\n",
      "Epoch [2090/5000], Loss: 0.4995\n",
      "Epoch [2100/5000], Loss: 0.4995\n",
      "Epoch [2110/5000], Loss: 0.4995\n",
      "Epoch [2120/5000], Loss: 0.4995\n",
      "Epoch [2130/5000], Loss: 0.4995\n",
      "Epoch [2140/5000], Loss: 0.4995\n",
      "Epoch [2150/5000], Loss: 0.4995\n",
      "Epoch [2160/5000], Loss: 0.4995\n",
      "Epoch [2170/5000], Loss: 0.4995\n",
      "Epoch [2180/5000], Loss: 0.4995\n",
      "Epoch [2190/5000], Loss: 0.4995\n",
      "Epoch [2200/5000], Loss: 0.4995\n",
      "Epoch [2210/5000], Loss: 0.4995\n",
      "Epoch [2220/5000], Loss: 0.4995\n",
      "Epoch [2230/5000], Loss: 0.4995\n",
      "Epoch [2240/5000], Loss: 0.4995\n",
      "Epoch [2250/5000], Loss: 0.4995\n",
      "Epoch [2260/5000], Loss: 0.4995\n",
      "Epoch [2270/5000], Loss: 0.4995\n",
      "Epoch [2280/5000], Loss: 0.4995\n",
      "Epoch [2290/5000], Loss: 0.4995\n",
      "Epoch [2300/5000], Loss: 0.4995\n",
      "Epoch [2310/5000], Loss: 0.4995\n",
      "Epoch [2320/5000], Loss: 0.4995\n",
      "Epoch [2330/5000], Loss: 0.4995\n",
      "Epoch [2340/5000], Loss: 0.4995\n",
      "Epoch [2350/5000], Loss: 0.4995\n",
      "Epoch [2360/5000], Loss: 0.4995\n",
      "Epoch [2370/5000], Loss: 0.4995\n",
      "Epoch [2380/5000], Loss: 0.4995\n",
      "Epoch [2390/5000], Loss: 0.4995\n",
      "Epoch [2400/5000], Loss: 0.4995\n",
      "Epoch [2410/5000], Loss: 0.4995\n",
      "Epoch [2420/5000], Loss: 0.4995\n",
      "Epoch [2430/5000], Loss: 0.4995\n",
      "Epoch [2440/5000], Loss: 0.4995\n",
      "Epoch [2450/5000], Loss: 0.4995\n",
      "Epoch [2460/5000], Loss: 0.4995\n",
      "Epoch [2470/5000], Loss: 0.4995\n",
      "Epoch [2480/5000], Loss: 0.4995\n",
      "Epoch [2490/5000], Loss: 0.4995\n",
      "Epoch [2500/5000], Loss: 0.4995\n",
      "Epoch [2510/5000], Loss: 0.4995\n",
      "Epoch [2520/5000], Loss: 0.4995\n",
      "Epoch [2530/5000], Loss: 0.4995\n",
      "Epoch [2540/5000], Loss: 0.4995\n",
      "Epoch [2550/5000], Loss: 0.4995\n",
      "Epoch [2560/5000], Loss: 0.4995\n",
      "Epoch [2570/5000], Loss: 0.4995\n",
      "Epoch [2580/5000], Loss: 0.4995\n",
      "Epoch [2590/5000], Loss: 0.4995\n",
      "Epoch [2600/5000], Loss: 0.4995\n",
      "Epoch [2610/5000], Loss: 0.4995\n",
      "Epoch [2620/5000], Loss: 0.4995\n",
      "Epoch [2630/5000], Loss: 0.4995\n",
      "Epoch [2640/5000], Loss: 0.4995\n",
      "Epoch [2650/5000], Loss: 0.4995\n",
      "Epoch [2660/5000], Loss: 0.4995\n",
      "Epoch [2670/5000], Loss: 0.4995\n",
      "Epoch [2680/5000], Loss: 0.4995\n",
      "Epoch [2690/5000], Loss: 0.4995\n",
      "Epoch [2700/5000], Loss: 0.4995\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2710/5000], Loss: 0.4995\n",
      "Epoch [2720/5000], Loss: 0.4995\n",
      "Epoch [2730/5000], Loss: 0.4995\n",
      "Epoch [2740/5000], Loss: 0.4995\n",
      "Epoch [2750/5000], Loss: 0.4995\n",
      "Epoch [2760/5000], Loss: 0.4995\n",
      "Epoch [2770/5000], Loss: 0.4995\n",
      "Epoch [2780/5000], Loss: 0.4995\n",
      "Epoch [2790/5000], Loss: 0.4995\n",
      "Epoch [2800/5000], Loss: 0.4995\n",
      "Epoch [2810/5000], Loss: 0.4995\n",
      "Epoch [2820/5000], Loss: 0.4995\n",
      "Epoch [2830/5000], Loss: 0.4995\n",
      "Epoch [2840/5000], Loss: 0.4995\n",
      "Epoch [2850/5000], Loss: 0.4995\n",
      "Epoch [2860/5000], Loss: 0.4995\n",
      "Epoch [2870/5000], Loss: 0.4995\n",
      "Epoch [2880/5000], Loss: 0.4995\n",
      "Epoch [2890/5000], Loss: 0.4995\n",
      "Epoch [2900/5000], Loss: 0.4995\n",
      "Epoch [2910/5000], Loss: 0.4995\n",
      "Epoch [2920/5000], Loss: 0.4995\n",
      "Epoch [2930/5000], Loss: 0.4995\n",
      "Epoch [2940/5000], Loss: 0.4995\n",
      "Epoch [2950/5000], Loss: 0.4995\n",
      "Epoch [2960/5000], Loss: 0.4995\n",
      "Epoch [2970/5000], Loss: 0.4995\n",
      "Epoch [2980/5000], Loss: 0.4995\n",
      "Epoch [2990/5000], Loss: 0.4995\n",
      "Epoch [3000/5000], Loss: 0.4995\n",
      "Epoch [3010/5000], Loss: 0.4995\n",
      "Epoch [3020/5000], Loss: 0.4995\n",
      "Epoch [3030/5000], Loss: 0.4995\n",
      "Epoch [3040/5000], Loss: 0.4995\n",
      "Epoch [3050/5000], Loss: 0.4995\n",
      "Epoch [3060/5000], Loss: 0.4995\n",
      "Epoch [3070/5000], Loss: 0.4995\n",
      "Epoch [3080/5000], Loss: 0.4995\n",
      "Epoch [3090/5000], Loss: 0.4995\n",
      "Epoch [3100/5000], Loss: 0.4995\n",
      "Epoch [3110/5000], Loss: 0.4995\n",
      "Epoch [3120/5000], Loss: 0.4995\n",
      "Epoch [3130/5000], Loss: 0.4995\n",
      "Epoch [3140/5000], Loss: 0.4995\n",
      "Epoch [3150/5000], Loss: 0.4995\n",
      "Epoch [3160/5000], Loss: 0.4995\n",
      "Epoch [3170/5000], Loss: 0.4995\n",
      "Epoch [3180/5000], Loss: 0.4995\n",
      "Epoch [3190/5000], Loss: 0.4995\n",
      "Epoch [3200/5000], Loss: 0.4995\n",
      "Epoch [3210/5000], Loss: 0.4995\n",
      "Epoch [3220/5000], Loss: 0.4995\n",
      "Epoch [3230/5000], Loss: 0.4995\n",
      "Epoch [3240/5000], Loss: 0.4995\n",
      "Epoch [3250/5000], Loss: 0.4995\n",
      "Epoch [3260/5000], Loss: 0.4995\n",
      "Epoch [3270/5000], Loss: 0.4995\n",
      "Epoch [3280/5000], Loss: 0.4995\n",
      "Epoch [3290/5000], Loss: 0.4995\n",
      "Epoch [3300/5000], Loss: 0.4995\n",
      "Epoch [3310/5000], Loss: 0.4995\n",
      "Epoch [3320/5000], Loss: 0.4995\n",
      "Epoch [3330/5000], Loss: 0.4995\n",
      "Epoch [3340/5000], Loss: 0.4995\n",
      "Epoch [3350/5000], Loss: 0.4995\n",
      "Epoch [3360/5000], Loss: 0.4995\n",
      "Epoch [3370/5000], Loss: 0.4995\n",
      "Epoch [3380/5000], Loss: 0.4995\n",
      "Epoch [3390/5000], Loss: 0.4995\n",
      "Epoch [3400/5000], Loss: 0.4995\n",
      "Epoch [3410/5000], Loss: 0.4995\n",
      "Epoch [3420/5000], Loss: 0.4995\n",
      "Epoch [3430/5000], Loss: 0.4995\n",
      "Epoch [3440/5000], Loss: 0.4995\n",
      "Epoch [3450/5000], Loss: 0.4995\n",
      "Epoch [3460/5000], Loss: 0.4995\n",
      "Epoch [3470/5000], Loss: 0.4995\n",
      "Epoch [3480/5000], Loss: 0.4995\n",
      "Epoch [3490/5000], Loss: 0.4995\n",
      "Epoch [3500/5000], Loss: 0.4995\n",
      "Epoch [3510/5000], Loss: 0.4995\n",
      "Epoch [3520/5000], Loss: 0.4995\n",
      "Epoch [3530/5000], Loss: 0.4995\n",
      "Epoch [3540/5000], Loss: 0.4995\n",
      "Epoch [3550/5000], Loss: 0.4995\n",
      "Epoch [3560/5000], Loss: 0.4995\n",
      "Epoch [3570/5000], Loss: 0.4995\n",
      "Epoch [3580/5000], Loss: 0.4995\n",
      "Epoch [3590/5000], Loss: 0.4995\n",
      "Epoch [3600/5000], Loss: 0.4995\n",
      "Epoch [3610/5000], Loss: 0.4995\n",
      "Epoch [3620/5000], Loss: 0.4995\n",
      "Epoch [3630/5000], Loss: 0.4995\n",
      "Epoch [3640/5000], Loss: 0.4995\n",
      "Epoch [3650/5000], Loss: 0.4995\n",
      "Epoch [3660/5000], Loss: 0.4995\n",
      "Epoch [3670/5000], Loss: 0.4995\n",
      "Epoch [3680/5000], Loss: 0.4995\n",
      "Epoch [3690/5000], Loss: 0.4995\n",
      "Epoch [3700/5000], Loss: 0.4995\n",
      "Epoch [3710/5000], Loss: 0.4995\n",
      "Epoch [3720/5000], Loss: 0.4995\n",
      "Epoch [3730/5000], Loss: 0.4995\n",
      "Epoch [3740/5000], Loss: 0.4995\n",
      "Epoch [3750/5000], Loss: 0.4995\n",
      "Epoch [3760/5000], Loss: 0.4995\n",
      "Epoch [3770/5000], Loss: 0.4995\n",
      "Epoch [3780/5000], Loss: 0.4995\n",
      "Epoch [3790/5000], Loss: 0.4995\n",
      "Epoch [3800/5000], Loss: 0.4995\n",
      "Epoch [3810/5000], Loss: 0.4995\n",
      "Epoch [3820/5000], Loss: 0.4995\n",
      "Epoch [3830/5000], Loss: 0.4995\n",
      "Epoch [3840/5000], Loss: 0.4995\n",
      "Epoch [3850/5000], Loss: 0.4995\n",
      "Epoch [3860/5000], Loss: 0.4995\n",
      "Epoch [3870/5000], Loss: 0.4995\n",
      "Epoch [3880/5000], Loss: 0.4995\n",
      "Epoch [3890/5000], Loss: 0.4995\n",
      "Epoch [3900/5000], Loss: 0.4995\n",
      "Epoch [3910/5000], Loss: 0.4995\n",
      "Epoch [3920/5000], Loss: 0.4995\n",
      "Epoch [3930/5000], Loss: 0.4995\n",
      "Epoch [3940/5000], Loss: 0.4995\n",
      "Epoch [3950/5000], Loss: 0.4995\n",
      "Epoch [3960/5000], Loss: 0.4995\n",
      "Epoch [3970/5000], Loss: 0.4995\n",
      "Epoch [3980/5000], Loss: 0.4995\n",
      "Epoch [3990/5000], Loss: 0.4995\n",
      "Epoch [4000/5000], Loss: 0.4995\n",
      "Epoch [4010/5000], Loss: 0.4995\n",
      "Epoch [4020/5000], Loss: 0.4995\n",
      "Epoch [4030/5000], Loss: 0.4995\n",
      "Epoch [4040/5000], Loss: 0.4995\n",
      "Epoch [4050/5000], Loss: 0.4995\n",
      "Epoch [4060/5000], Loss: 0.4995\n",
      "Epoch [4070/5000], Loss: 0.4995\n",
      "Epoch [4080/5000], Loss: 0.4995\n",
      "Epoch [4090/5000], Loss: 0.4995\n",
      "Epoch [4100/5000], Loss: 0.4995\n",
      "Epoch [4110/5000], Loss: 0.4995\n",
      "Epoch [4120/5000], Loss: 0.4995\n",
      "Epoch [4130/5000], Loss: 0.4995\n",
      "Epoch [4140/5000], Loss: 0.4995\n",
      "Epoch [4150/5000], Loss: 0.4995\n",
      "Epoch [4160/5000], Loss: 0.4995\n",
      "Epoch [4170/5000], Loss: 0.4995\n",
      "Epoch [4180/5000], Loss: 0.4995\n",
      "Epoch [4190/5000], Loss: 0.4995\n",
      "Epoch [4200/5000], Loss: 0.4995\n",
      "Epoch [4210/5000], Loss: 0.4995\n",
      "Epoch [4220/5000], Loss: 0.4995\n",
      "Epoch [4230/5000], Loss: 0.4995\n",
      "Epoch [4240/5000], Loss: 0.4995\n",
      "Epoch [4250/5000], Loss: 0.4995\n",
      "Epoch [4260/5000], Loss: 0.4995\n",
      "Epoch [4270/5000], Loss: 0.4995\n",
      "Epoch [4280/5000], Loss: 0.4995\n",
      "Epoch [4290/5000], Loss: 0.4995\n",
      "Epoch [4300/5000], Loss: 0.4995\n",
      "Epoch [4310/5000], Loss: 0.4995\n",
      "Epoch [4320/5000], Loss: 0.4995\n",
      "Epoch [4330/5000], Loss: 0.4995\n",
      "Epoch [4340/5000], Loss: 0.4995\n",
      "Epoch [4350/5000], Loss: 0.4995\n",
      "Epoch [4360/5000], Loss: 0.4995\n",
      "Epoch [4370/5000], Loss: 0.4995\n",
      "Epoch [4380/5000], Loss: 0.4995\n",
      "Epoch [4390/5000], Loss: 0.4995\n",
      "Epoch [4400/5000], Loss: 0.4995\n",
      "Epoch [4410/5000], Loss: 0.4995\n",
      "Epoch [4420/5000], Loss: 0.4996\n",
      "Epoch [4430/5000], Loss: 0.4996\n",
      "Epoch [4440/5000], Loss: 0.4995\n",
      "Epoch [4450/5000], Loss: 0.4995\n",
      "Epoch [4460/5000], Loss: 0.4995\n",
      "Epoch [4470/5000], Loss: 0.4995\n",
      "Epoch [4480/5000], Loss: 0.4995\n",
      "Epoch [4490/5000], Loss: 0.4995\n",
      "Epoch [4500/5000], Loss: 0.4995\n",
      "Epoch [4510/5000], Loss: 0.4995\n",
      "Epoch [4520/5000], Loss: 0.4995\n",
      "Epoch [4530/5000], Loss: 0.4995\n",
      "Epoch [4540/5000], Loss: 0.4995\n",
      "Epoch [4550/5000], Loss: 0.4995\n",
      "Epoch [4560/5000], Loss: 0.4995\n",
      "Epoch [4570/5000], Loss: 0.4995\n",
      "Epoch [4580/5000], Loss: 0.4995\n",
      "Epoch [4590/5000], Loss: 0.4995\n",
      "Epoch [4600/5000], Loss: 0.4995\n",
      "Epoch [4610/5000], Loss: 0.4995\n",
      "Epoch [4620/5000], Loss: 0.4995\n",
      "Epoch [4630/5000], Loss: 0.4995\n",
      "Epoch [4640/5000], Loss: 0.4995\n",
      "Epoch [4650/5000], Loss: 0.4995\n",
      "Epoch [4660/5000], Loss: 0.4995\n",
      "Epoch [4670/5000], Loss: 0.4995\n",
      "Epoch [4680/5000], Loss: 0.4995\n",
      "Epoch [4690/5000], Loss: 0.4995\n",
      "Epoch [4700/5000], Loss: 0.4995\n",
      "Epoch [4710/5000], Loss: 0.4995\n",
      "Epoch [4720/5000], Loss: 0.4995\n",
      "Epoch [4730/5000], Loss: 0.4995\n",
      "Epoch [4740/5000], Loss: 0.4995\n",
      "Epoch [4750/5000], Loss: 0.4995\n",
      "Epoch [4760/5000], Loss: 0.4995\n",
      "Epoch [4770/5000], Loss: 0.4995\n",
      "Epoch [4780/5000], Loss: 0.4995\n",
      "Epoch [4790/5000], Loss: 0.4995\n",
      "Epoch [4800/5000], Loss: 0.4995\n",
      "Epoch [4810/5000], Loss: 0.4995\n",
      "Epoch [4820/5000], Loss: 0.4995\n",
      "Epoch [4830/5000], Loss: 0.4995\n",
      "Epoch [4840/5000], Loss: 0.4995\n",
      "Epoch [4850/5000], Loss: 0.4995\n",
      "Epoch [4860/5000], Loss: 0.4995\n",
      "Epoch [4870/5000], Loss: 0.4995\n",
      "Epoch [4880/5000], Loss: 0.4995\n",
      "Epoch [4890/5000], Loss: 0.4995\n",
      "Epoch [4900/5000], Loss: 0.4995\n",
      "Epoch [4910/5000], Loss: 0.4995\n",
      "Epoch [4920/5000], Loss: 0.4995\n",
      "Epoch [4930/5000], Loss: 0.4995\n",
      "Epoch [4940/5000], Loss: 0.4995\n",
      "Epoch [4950/5000], Loss: 0.4995\n",
      "Epoch [4960/5000], Loss: 0.4995\n",
      "Epoch [4970/5000], Loss: 0.4995\n",
      "Epoch [4980/5000], Loss: 0.4995\n",
      "Epoch [4990/5000], Loss: 0.4995\n",
      "Epoch [5000/5000], Loss: 0.4995\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate training data\n",
    "x = np.linspace(0, 2*np.pi, 1000)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x_tensor = torch.tensor(x.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "y_tensor = torch.tensor(y.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "\n",
    "# Define the RNN model\n",
    "class RNNSin(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(RNNSin, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "        \n",
    "    def forward(self, input):\n",
    "        output, _ = self.rnn(input)\n",
    "        output = self.fc(output[:, -1, :])\n",
    "        return output\n",
    "\n",
    "# Set hyperparameters\n",
    "input_size = 1\n",
    "hidden_size = 200\n",
    "output_size = 1\n",
    "learning_rate = 0.01\n",
    "num_epochs = 5000\n",
    "\n",
    "# Initialize the RNN model\n",
    "model = RNNSin(input_size, hidden_size, output_size)\n",
    "\n",
    "# Loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training loop\n",
    "losses = []\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass\n",
    "    output = model(x_tensor)\n",
    "    loss = criterion(output, y_tensor)\n",
    "    \n",
    "    # Backward pass and optimization\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    # Print progress\n",
    "    if (epoch+1) % 10 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "    \n",
    "    # Store the loss\n",
    "    losses.append(loss.item())\n",
    "\n",
    "# Plot the training loss\n",
    "plt.plot(losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss')\n",
    "plt.show()\n",
    "\n",
    "# Test the model\n",
    "x_test = np.linspace(0, 2*np.pi, 200)\n",
    "x_test_tensor = torch.tensor(x_test.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "with torch.no_grad():\n",
    "    y_pred = model(x_test_tensor)\n",
    "\n",
    "# Plot the true sin(x) and predicted sin(x)\n",
    "plt.plot(x, y, label='True sin(x)')\n",
    "plt.plot(x_test, y_pred.squeeze().numpy(), label='Predicted sin(x)')\n",
    "plt.legend()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "plt.title('sin(x) Approximation')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7277b324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEWCAYAAABIVsEJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABBeUlEQVR4nO3dd3hUZdrH8e+dTiC0hB4goTcBMYCAIIIgKoIuNlwVFOyCq7squq7d1bVhFxFYUbGiKKsgAoIgiBB6hxBaIiUklEAIaff7xwy8Q0hIIcmZSe7Pdc3FzGnzmwC55znPOc8jqooxxhhTVH5OBzDGGOObrIAYY4wpFisgxhhjisUKiDHGmGKxAmKMMaZYrIAYY4wpFisgxieIyOMiMqEI27cRkVgRkUJs+42IXH5uCb2DiBwVkSZl9F7jRORfZfFexjuJ3QdiyiMR+Qb4WlW/KMS2XYD3VfWCAraLBrYBH6jqPSWT1DeIyHBgpKpe5HQW4z2sBWLKHRGpB1wCfFeY7VV1KVBVRGIK2PRW4CBwg4gEn1PIfIiL/b80PsH+oRqvIiKPikiiiKSKyGYR6ete/rSIfOp+HiUiKiLDRGSXiBwQkX96HKYfsEJV093bNxWRFBHp5H5dX0SSRKS3xz7zgSvPkktwFZAngEzgqlzrVURGi0i8O88rJwuBiAwXkUUi8o6IHBaRTSc/l3v9fBF5QUQWAWlAExHpLiLL3NsvE5Hu7m1vEJHtIlLV/fpyEdkrIrU8cjRzP/9IRN4TkZnuU1uLRKSuiLwhIgfdOc73yDFGRLa5f/YbROQa9/LWwDigm/s4hzyO/7zH/neISJz7Zz1dROrn+vncLSJbReSQiLxbmNOLxrtZATFeQ0RaAvcDnVU1DLgM2HGWXS4CWgJ9gSfdv+gAzgM2n9xIVbcBjwKfikgo8F9gsqrO9zjWRqBDAe8VCXwBfAUMy2Oba4AYoBMwGLjdY11XXKe/IoCngG9FpKbH+luAO4EwIBX4EXgLCAdeB34UkXBV/RJYDLwlIuHARFynlpLyyX09rqIXAZwAfgdWuF9PdR/7pG1AT6Aa8Ayun1c9Vd0I3A38rqpVVLV67jcRkT7Ai+73qwfsdP+sPA0EOgPt3dtdlk9m4yOsgBhvkg0EA21EJFBVd7h/+efnGVU9rqqrgdX8fwGojuuX8Cmq+iEQB/yB6xecZ4sF9/bVz/Jew4CZqnoQ+AwYICK1c23zH1VNUdVdwBvAUI91+4E3VDXTXQQ2c3qL5yNVXa+qWUB/YKuqfqKqWar6ObCJ/2/13Af0wdVq+p+q/nCW3NNUdbm7NTYNSFfVj1U1G/gSONUCUdWvVfVPVc1xZ9wKdDnLsT39FZikqitU9QTwGK4WS5THNi+p6iH3z2ce0LGQxzZeygqI8RqqGgf8DXga2C8iX3ieBsnDXo/naUAV9/ODuL7J5/Yh0A542/1LzlMYcCivNxGRSsB1wBR3zt+BXcBNuTbd7fF8J+CZPVFPv2Il93rPfeu713vaCTRwv/8h4Gv3Z3ktr8we9nk8P57H65M/M0TkVhFZ5T7FdMh9/IgCjp9nZlU9CiSfzOyW39+X8VFWQIxXUdXP3Ff6NAYU+E8xDrMGaOG5QESq4GoVTASeznX6CKA1rlZMXq4BqgLvufsb9uL6xZj7NFZDj+eNgD89XjfIdc4/93rP4vInrs/vqRGQ6P4sHXGdHvsc12mucyYijXEV2PuBcPdpqnXAycwFXa55WmYRqYzr9FtiSeQz3skKiPEaItJSRPq4r3BKx/UNOacYh5oNdBKREI9lbwKxqjoSV//CuFz7XAzMzOd4w4BJuPpWOrofPYAOInKex3YPi0gNEWkIPIDrFNFJtYHRIhIoItfhKlgz8nm/GUALEblJRAJE5AagDfCD+zN9CjwO3IarMN2bz3GKojKuIpEEICK34WqBnLQPiBSRoHz2/xy4TUQ6uv/+/g38oao7SiCb8VJWQIw3CQZeAg7gOt1RG9e59CJR1X3AL7g6shGRwcAA4OS9Gw/hKjB/da/vDBx1X857GhFpgKuT/g1V3evxWA78xOmtkO+B5cAqXEVqose6P4Dm7s/2AnCtqibnkz8ZV4fz33GdBnoEGKiqB3B1VO9W1ffdp+FuBp4XkeZF+BHl9Z4bcJ0O+x1XsTgPWOSxyS/AemCviBzIY/85wL+Ab4A9QFPgxnPJZLyf3UhoyiURaQNMBrpoAf/IxXXT4URVza9FUJj3U6C5ux8n97rh2E14phwKcDqAMaXB/Y26cyG3HVLKcYwpl+wUljHGmGKxU1jGGGOKxVogxhhjiqVC9YFERERoVFSU0zGMMcanLF++/ICq1sq9vEIVkKioKGJjY52OYYwxPkVEco+MANgpLGOMMcVkBcQYY0yxWAExxhhTLBWqD8QYUzoyMzNJSEggPT3d6SjmHISEhBAZGUlgYGChtrcCYow5ZwkJCYSFhREVFYVNNOibVJXk5GQSEhKIjo4u1D6OnsISkUkisl9E1uWzXkTkLfc0mWvEPSWpe90w9/SYW0Ukr9nhjDFlJD09nfDwcCsePkxECA8PL1Ir0uk+kI9wjZKan8txjWDaHNd0n+8DuOdyeArXNKFdgKdEpEapJjXGnJUVD99X1L9DR09hqeqCXFNe5jYY+Ng9muoSEakuIvWA3sBsVU0BEJHZuArR56Uc2eTh2IksNu9LZXdKGgePZXAkPQt/PyHI34/qoYE0rBlKVHhl6lQNtl8yxpQj3t4H0oDTp/pMcC/Lb/kZROROXK0XGjVqVDopK5j0zGwWbj3Agi1JLIo7QPyBY4Xar27VEGKiatCzeQSXta1L9dD85iYyxvgCby8g50xVxwPjAWJiYmzkyHOwLvEwny/dxfTVf5KankVokD/dmoRzzfkNaFk3jCa1KlMjNIhqlQLJUUjPyubgsQx2paQRn3SM5TsPsnR7Cj+s2cM/p62jZ/MIbu0WxcUtauHnZy0TU3zJycn07dsXgL179+Lv70+tWq6RN5YuXUpQUOl8WRk3bhyhoaHceuutZ91u5cqVvPPOO0ycODHfbd555x1CQ0O5/fbbSzpmqfH2ApLI6fNMR7qXJeI6jeW5fH6ZpapglsQn8+68OBZuPUBIoB+Xt6vHXzo1oEt0TYID/PPdLyjAj6ohgTQOr0zP5rUY1j0KVWVd4hF+XLuHaSsTuO2jZTSJqMzdFzflL50aEODvdLec8UXh4eGsWrUKgKeffpoqVarwj3/849T6rKwsAgJK/tfd3XffXajt/v3vf/PEE0+cdZvbb7+dHj16WAEpQdOB+0XkC1wd5odVdY+IzAL+7dFx3p9iTH1qzm5b0lGe/2ED8zYnEVEliEcHtOKvFzaiakjhrhHPi4hwXmQ1zousxt/7t2Dmur18uCCeR75Zw/iF8Tw6oBX92tQpwU9hytoz/1vPhj+PlOgx29SvylNXtS3SPsOHDyckJISVK1fSo0cPqlatelphadeuHT/88ANRUVF8+umnvPXWW2RkZNC1a1fee+89/P1P/3I0ZswYpk+fTkBAAP379+fVV189rVj17t2brl27Mm/ePA4dOsTEiRPp2bMnqamprFmzhg4dOgDwwAMPEB4ezpNPPsmsWbN44YUXmD9/PqGhoURFRbF06VK6dOlSMj+4UuZoARGRz3G1JCJEJAHXlVWBAKo6DpgBXAHEAWnAbe51KSLyHLDMfahnT3aom3OXnpnN2DlbmLhwO5UC/Xn8ilbc2i2KkMD8WxvFEejvx6AO9bmqfT1mrd/Ly7M2c8fHsfRvU4dnBrelXrVKJfp+puJJSEhg8eLF+Pv78/TTT+e5zcaNG/nyyy9ZtGgRgYGB3HvvvUyZMuW001LJyclMmzaNTZs2ISIcOnQoz2NlZWWxdOlSZsyYwTPPPMOcOXOIjY2lXbt2p7Z58cUX6dy5Mz179mT06NHMmDEDPz9XyzsmJoaFCxdaASkMVR1awHoF7stn3SRgUmnkqsjWJhzmoa9WsXX/Ua6PieSRAa2IqBJcqu8pIgxoV49LW9dh4m/bGTtnC/1eX8BTV7Xh2gsi7cotH1PUlkJpuu66685oSeQ2d+5cli9fTufOrhmQjx8/Tu3atU/bplq1aoSEhDBixAgGDhzIwIED8zzWX/7yFwAuuOACduzYAcCePXtO9ccAhIaG8uGHH9KrVy/Gjh1L06ZNT62rXbs2mzZtKvLndIq3n8IyZURVGfdrPK/+vJmIKkFMvr0LF7c4Y/j/UhXg78ddFzdlQLu6PDJ1DQ9PXcPibck8d3U7qgTbP1VTdJUrVz71PCAggJycnFOvT94wp6oMGzaMF198Md/jBAQEsHTpUubOncvUqVN55513+OWXX87YLjjY9WXL39+frKwsACpVqnTGzXlr164lPDycP//887Tl6enpVKrkOy1v67E0HD2Rxb1TVvCfnzYxoG1dfv7bxWVePDw1Dq/MZ3dcyEP9WvD9qkSuevs34vanOpbHlA9RUVGsWLECgBUrVrB9+3YA+vbty9SpU9m/fz8AKSkp7Nx5+vQXR48e5fDhw1xxxRWMHTuW1atXF/p9W7duTVxc3KnXO3fu5LXXXmPlypXMnDmTP/7449S6LVu2nHa6y9tZAangdiWncfW7i5i1fi//vKI179x0PtVCi99JXlL8/YTRfZvz+R0XkpqexTXvLWbh1iSnYxkfNmTIEFJSUmjbti3vvPMOLVq0AKBNmzY8//zz9O/fn/bt29OvXz/27Nlz2r6pqakMHDiQ9u3bc9FFF/H6668X+n1btWrF4cOHSU1NRVUZMWIEr776KvXr12fixImMHDnyVAtl0aJF9OvXr+Q+dCkTVzdDxRATE6M2I+H/W5twmNs+WkpWjvLeTZ3o3izC6Uh5SjiYxsjJsWzdf5RnBrXl5gsbOx3J5LJx40Zat27tdAyvNXbsWMLCwhg5cmS+26xcuZLXX3+dTz75pAyTnSmvv0sRWa6qMbm3tRZIBbVwaxI3jv+d4AB/pt7d3WuLB0BkjVCm3tOdi1vU4onv1vHuvLiCdzLGi9xzzz2n+kfyc+DAAZ577rkySlQyrIBUQHM27OP2j5bRsGYo397bnWa1qzgdqUBVggMYf8sFXHN+A16ZtZmXZm6iIrWejW8LCQnhlltuOes2/fr1IyoqqmwClRC7tKWCmbNhH/dMWU6belX5eERXqlVyvr+jsAL8/Xjtug6EBvkz7tdtpGdm89RVbewyX2McYgWkApm70XeLx0l+fsLzV7cjOMCfSYu2UznYn4cva+V0LGMqJCsgFcTibQe459MVtPbh4nGSiPCvga05npnNu/O2ERoUwH2XNHM6ljEVjhWQCmDDn0e46+PlNA4P5ePbu/h08ThJxNUSScvI4pVZmwkLCeDWblFOxzKmQrFO9HJud0oaw/+7lCohAUy+vUu5moPD30949boOXNq6Dk9PX8+cDfucjmQc5O/vT8eOHWnXrh3XXXcdaWlpxT7W8OHDmTp1KgAjR45kw4YN+W47f/58Fi9eXOT3iIqK4sCBA4Xa9sknn2TOnDkFbvfdd9/x7LPPnnWbf/zjH3neRV8cVkDKscNpmQz771LSM7OZfHsX6lf3nSESCivQ34+3hnakXYNqjPp8JWsTDjsdyTikUqVKrFq1inXr1hEUFMS4ceNOW39yaJGimjBhAm3atMl3fXELSFE8++yzXHrppQVu9/LLL3PvvfeedZtRo0bx0ksvlUguO4VVTmVl53D/5yvYnZLGlJEX0qJOmNORSk1oUAAThsVwzbuLGTF5Gd/d16NcFkufMXMM7F1bssesex5cXvhfej179mTNmjXMnz+ff/3rX9SoUYNNmzaxceNGxowZw/z58zlx4gT33Xcfd911F6rKqFGjmD17Ng0bNjxtAqrevXvz6quvEhMTw08//cTjjz9OdnY2ERERTJw4kXHjxuHv78+nn37K22+/TatWrbj77rvZtWsXAG+88QY9evQgOTmZoUOHkpiYSLdu3fK8DD07O5sRI0YQGxuLiHD77bfz4IMPMnz4cAYOHMi1115LVFQUw4YN43//+x+ZmZl8/fXXtGrVii1bthAcHExEhOuersGDBzNkyBBuvfVWPvjgAxYsWMCUKVNo3LgxycnJ7N27l7p1657TX4sVkHLqxZmbWLj1AP8Zch5doms6HafU1Q4LYdLwzlz7/mJGTI7l23u6UymoZIefN74hKyuLmTNnMmDAAMA17tW6deuIjo5m/PjxVKtWjWXLlnHixAl69OhB//79WblyJZs3b2bDhg3s27ePNm3anDGxU1JSEnfccQcLFiwgOjqalJQUatasyd13333aPCM33XQTDz74IBdddBG7du3isssuY+PGjTzzzDNcdNFFPPnkk/z44495zk64atUqEhMTWbduHUC+w8ZHRESwYsUK3nvvPV599VUmTJjAokWL6NSp06ltxo8fT48ePYiOjua1115jyZIlp9Z16tSJRYsWMWTIkHP6WVsBKYe+jt3NxN+2M7x7FDd0rjjzwLesG8ZbQ8/n9snL+Oe0tbx2fQe7R8QJRWgplKTjx4/TsWNHwNUCGTFiBIsXL6ZLly5ER0cD8PPPP7NmzZpT/RuHDx9m69atLFiwgKFDh+Lv70/9+vXp06fPGcdfsmQJvXr1OnWsmjXz/mI2Z86c0/pMjhw5wtGjR1mwYAHffvstAFdeeSU1atQ4Y98mTZoQHx/PqFGjuPLKK+nfv3+e7+E5bPzJY+YeNr5OnTo8++yzXHLJJUybNu20vLVr1z5jJODisAJSzqzcdZB/TltH96bh/PPKijc20SWtavO3vi0YO2cLHRpWZ1j3KKcjmTJysg8kN88h3VWVt99+m8suu+y0bWbMmFFiOXJycliyZAkhISFF3rdGjRqsXr2aWbNmMW7cOL766ismTTpz2qP8ho0/fPj0PsDSHjbe0U50ERkgIptFJE5ExuSxfqyIrHI/tojIIY912R7rppdpcC91KC2D+z9bSe2qwbx7UycCK+j84qP6NKNvq9o898MGYnfYRJXm/1122WW8//77ZGZmAq7h048dO0avXr348ssvyc7OZs+ePcybN++MfS+88EIWLFhwahj4lBTXv62wsDBSU/9/uoH+/fvz9ttvn3p9sqj16tWLzz77DICZM2dy8ODBM97jwIED5OTkMGTIEJ5//vlTw88XRu5h45cuXcrMmTNZuXIlr7766qncJz93SQwb79hvGBHxB94FLgfaAENF5LRLHVT1QVXtqKodgbeBbz1WHz+5TlUHlVVub6Wq/OPr1exPTefdmzpRo3L5uVy3qPz8hNdv6EhkjUrcM2UFSaknnI5kvMTIkSNp06YNnTp1ol27dtx1111kZWVxzTXX0Lx5c9q0acOtt95Kt27dzti3Vq1ajB8/nr/85S906NCBG264AYCrrrqKadOm0bFjRxYuXMhbb71FbGws7du3p02bNqeuBnvqqadYsGABbdu25dtvv6VRozNPLycmJtK7d286duzIzTfffNZJrnLr1asXK1euRFU5ceIEd9xxB5MmTaJ+/fq89tpr3H777agqmZmZxMXFERNzxuC6RaeqjjyAbsAsj9ePAY+dZfvFQD+P10eL+p4XXHCBllcfLtimjR/9QSf9Fu90FK+xcc9hbfHPGXrrxD80OzvH6Tjl2oYNG5yOYFR19OjROnv27LNu8+233+oTTzyR7/q8/i6BWM3jd6qT5zgaALs9Xie4l51BRBoD0YDn3S8hIhIrIktE5Or83kRE7nRvF5uUVD4nJFqx6yAvzdzEZW3rMNzO+Z/Sqm5VnhjYhl+3JDFp0faCdzDGxz3++OMF3kCZlZXF3//+9xJ5P185SX4jMFVVsz2WNVbXBCc3AW+ISNO8dlTV8aoao6oxnlcolBep6ZmM/nwldauF8PK1dtVRbjd3bUT/NnX4z0+b7CbDUqY2vL7j6tSpw6BBZz+jf91111G9evU81xX179DJApIINPR4Helelpcbgc89F6hqovvPeGA+cH7JR/R+z/5vA38eOs6bN55fLsa4KmkiwsvXtieiSjCjPl/B0RPFuxvZnF1ISAjJyclWRHyYqpKcnFykq8ecvIx3GdBcRKJxFY4bcbUmTiMirYAawO8ey2oAaap6QkQigB7Ay2WS2ovMWr+Xr5cncP8lzbig8ZnXlBuX6qFBvHFDR4Z+uITnf9jAS0PaOx2p3ImMjCQhIYHyepq4oggJCSEyMrLQ2ztWQFQ1S0TuB2YB/sAkVV0vIs/i6rA5eWnujcAXevpXm9bAByKSg6sV9ZKq5j/aWTmUlHqCx75dS7sGVRndt7nTcbxe1ybh3NGrCR/8Gs+AdnXp3bK205HKlcDAwFM32JmKQypSkzMmJkZjY2OdjnHOVJWRk2P5Le4AP4y6iObleJyrkpSemc1Vb/9GanoWsx7sZaf8jCkkEVnu7nM+ja90ohsPX8cmMHfTfh4d0MqKRxGEBPrz2vUdSDp6gmf/V6EarMaUCisgPmbfkXSe+3EDXaNr2iW7xdA+sjr39m7KNysSmG3zhxhzTqyA+Jgnv19HRlYOLw1pj5+fXbJbHKP6NKdV3TAe+3Yth9IynI5jjM+yAuJDZq7dw6z1+3iwXwuiIyoXvIPJU1CAH69e14GDaRm8NHOT03GM8VlWQHzE4bRMnpy+nnYNqjLyIrva5Vy1a1CNERdF88Wy3fwRn+x0HGN8khUQH/HCjA2kHMvgP0PaE1BBR9ktaX+7tDmRNSrx2LS1nMjKLngHY8xp7DeRD1gcd4CvYhO4s1cT2tav5nScciM0KIDnr25HfNIx3p+/zek4xvgcKyBe7kRWNk98v47G4aE8YDcMlrjeLWtzVYf6vDdvG3H7jzodxxifYgXEy038bTvxScd4elBbQgJtju/S8OTANoQE+vH4tLU2lpMxRWAFxIslHjrO23PjuKxtHS6xoTdKTa2wYB67ojVLt6fw3ar8xvM0xuRmBcSLPfe/DSjKk1e1dTpKuXdDTEM6RFbj3zM2kZqe6XQcY3yCFRAvNX/zfn5av5dRfZrToHolp+OUe35+wjOD25GUeoK3f4kreAdjjBUQb5Semc1T09fTpFZl7ujZxOk4FUbHhtW5IaYhk37bbh3qxhSCFRAv9OGCeHYmp/HsoHYEBdhfUVl6eEBLKgX588z/1luHujEFsN9OXmbv4XTem7+NK86ry0XNI5yOU+FEVAnm7/1asHDrAWatt8EWjTkbKyBe5uVZm8jOUR67vLXTUSqsmy9sTKu6YTz3wwaOZ9gd6sbkx9ECIiIDRGSziMSJyJg81g8XkSQRWeV+jPRYN0xEtrofw8o2eelYvfsQ365IZETPaBrWDHU6ToUV4O/H04PaknjoOBMWxjsdxxiv5VgBERF/4F3gcqANMFRE2uSx6Zeq2tH9mODetybwFNAV6AI85Z4n3WepKs/9sIGIKkHc27up03EqvAubhDOgbV3e/3Ub+1PTnY5jjFdysgXSBYhT1XhVzQC+AAYXct/LgNmqmqKqB4HZwIBSylkmfly7h9idB/lH/5aEhdhUq95gzOWtyMzO4fWftzgdxRiv5GQBaQDs9nid4F6W2xARWSMiU0WkYRH3RUTuFJFYEYlNSkoqidwlLj0zmxdnbKJ1vapcF9Ow4B1MmYiKqMwtF0bxVexuNu094nQcY7yOt3ei/w+IUtX2uFoZk4t6AFUdr6oxqhpTq1atEg9YEib+tp3EQ8f518DW+Nssg15ldN9mhIUE8sKPG52OYozXcbKAJAKeX7cj3ctOUdVkVT3hfjkBuKCw+/qK/anpvDcvjv5t6tC9qV22622qhwYxum9zFm49wPzN+52OY4xXcbKALAOai0i0iAQBNwLTPTcQkXoeLwcBJ78GzgL6i0gNd+d5f/cyn/PW3K2cyMrhsSvssl1vdcuFjYkKD+WFHzeSlZ3jdBxjvIZjBURVs4D7cf3i3wh8parrReRZERnk3my0iKwXkdXAaGC4e98U4DlcRWgZ8Kx7mU+JTzrK50t3c1PXRjbHuRcLCvBjzOWt2Lr/KF/G7i54B2MqCKlIwzXExMRobGys0zFOuW/KCuZt3s+vD19CrbBgp+OYs1BVbvhgCfEHjvLrw5dQOTjA6UjGlBkRWa6qMbmXe3snerm1avchfly7hzt6NrHi4QNEhDFXtOLA0Qwm/bbd6TjGeAUrIA5QVV6csZHwykHc0ctG2/UVnRrVoH+bOnywIJ6UYxlOxzHGcVZAHDB/SxJ/bE9hdN/mVLFTIT7l4ctakpaRxXvzbM4QY6yAlLHsHOU/MzfRODyUoV0aOR3HFFHzOmEM6RTJx7/vJPHQcafjGOMoKyBl7PtViWzam8o/+re0uT581N/6tQCBN2bbECemYrPfYGUoPTOb137ewnkNqnHlefUK3sF4pQbVK3HrhY35ZkUCW/elOh3HGMdYASlDU/7YReKh44y5vBV+NmSJT7vvkmZUDgrglVmbnY5ijGOsgJSRYyeyeH9+HN2bhtOjmQ1Z4utqVA7iroub8POGfSzfedDpOMY4wgpIGZn8+w4OHM3g7/1bOh3FlJDbL4omokow//lpk82fbiokKyBl4Eh6Jh/8Gk+fVrW5oLFPz3tlPIQGBTC6bzOWbk9h4dYDTscxpsxZASkDExdu5/DxTB7q18LpKKaE3dC5IQ2qV+L12VusFWIqHCsgpezgsQwm/rady9vVpV2Dak7HMSUsOMCfUX2asWr3IebZcO+mgrECUso+WBDPsYwsHrTWR7k15IJIGtUMtVaIqXCsgJSi/anpfLR4O4M71KdFnTCn45hSEujvxwN9m7Mu8Qiz1u9zOo4xZcYKSCl6f/42MrOVBy611kd5N7hjfZpEVGbs7C3k5FgrxFQMjhYQERkgIptFJE5ExuSx/iER2SAia0Rkrog09liXLSKr3I/pufd12p+HjjNlyS6u7RRpk0VVAAH+fjxwaXM270vlx7V7nI5jTJlwrICIiD/wLnA50AYYKiJtcm22EohR1fbAVOBlj3XHVbWj+zEIL/POvDgUZVTfZk5HMWXkqvb1aVGnCm/M2UK2tUJMBeBkC6QLEKeq8aqaAXwBDPbcQFXnqWqa++USILKMMxbLruQ0vlq2mxs7NyKyRqjTcUwZ8fMTHry0BduSjjF9daLTcYwpdU4WkAaA5wTTCe5l+RkBzPR4HSIisSKyRESuzm8nEbnTvV1sUlLSOQUurDfnbsXfT7i/j7U+KprL2talTb2qvDlnK1nZOU7HMaZU+UQnuojcDMQAr3gsbuyeo/cm4A0RaZrXvqo6XlVjVDWmVq1apZ51x4FjfLcqkZsvbEydqiGl/n7Gu/j5CQ/2a8GO5DS+XWGtEFO+OVlAEoGGHq8j3ctOIyKXAv8EBqnqiZPLVTXR/Wc8MB84vzTDFtY78+II8BPuutimqq2oLm1dmw6R1Xhz7lYysqwVYsovJwvIMqC5iESLSBBwI3Da1VQicj7wAa7isd9jeQ0RCXY/jwB6ABvKLHk+diWnMW1lIjd1bUTtMGt9VFQirlZI4qHjfLMiwek4xpQaxwqIqmYB9wOzgI3AV6q6XkSeFZGTV1W9AlQBvs51uW5rIFZEVgPzgJdU1fEC8u68OPz9hLsvzvNsmqlALm5Riw4Nq/PuvDgyrS/ElFMBTr65qs4AZuRa9qTH80vz2W8xcF7ppiua3SlpfLMigb92bWR9HwYR4W99m3PbR8v4dkUCN3Ru5HQkY0qcT3Si+4L35sfhJ8Ldva31YVx6t6xF+8hqvGOtEFNOWQEpAQkH05i6PIEbOjekXrVKTscxXkJEGN2nObtTjjNtpV2RZcofKyAl4P352wC4x1ofJpe+rWvTrkFV3p0XZ/eFmHLHCsg5+vPQcb6K3c11MQ2pX91aH+Z0J1shO5PT+H7Vn07HMaZEWQE5R+N+dbU+7rXWh8lHvzZ1aFOvKu9YK8SUM1ZAzsHew+l8sXQ3114QaWNemXyJCKP7Nmf7gWP8b421Qkz5YQXkHIz7dRs5qtzb28a8MmfXv00dWtUN4+1f4mykXlNuWAEppn1H0vls6S7+0qkBDWta68OcnZ+fqxUSn3SMH6wVYsqJQhUQEfETkfNF5EoR6SMitUs7mLf74Nd4snOU+y9p7nQU4yMGtK1LyzrWCjHlx1kLiIg0FZHxQBzwEjAUuBeY4x5G/TYRqXCtmP2p6Uz5YyfXnN+ARuHW+jCF4+cnjOrbjLj9R5lhsxaacqCgX/7PA58CTVX1MlW9WVWvdc8QOAioBtxS2iG9zfhf48nMzuG+S6zvwxTNFe3q0bx2Fd7+ZavNnW583lkLiKoOVdUFqnrGv3RV3a+qb6jq5NKL530OHD3Bp3/s5OqODWyuc1Nkfu6JxrbsO8rMdXudjmPMOSlsH8hzIhLg8bqqiPy39GJ5rw8XxJORlWOzDZpiG9i+Pk1rVeatudYKMb6tsP0XAcAfItJeRPrhmstjeenF8k7JR0/w8e87GdShPk1qVXE6jvFR/n7CqD7N2bwvlVnrrRVifFehCoiqPgY8AvwBTAauVNV3SjOYN/pw4XbSs7Kt9WHO2VUd6tMkojJvWivE+LDCnsLqBbwFPItr+ti3RaR+KebyOinHMvj49x0MbF+fZrXDnI5jfJy/uy9k095UZm/c53QcY4qlsKewXgWuU9UXVfUm4EPgl3N9cxEZICKbRSRORMbksT5YRL50r/9DRKI81j3mXr5ZRC471ywFmfhbPMczsxllrQ9TQgZ1qE9UeChvzd1KHtepGOP1CltAunlOGauq3+Kah7zYRMQfeBe4HGgDDBWRNrk2GwEcVNVmwFjgP+592+CaQ70tMAB4z328UnEoLYPJi3dyRbt6tKhjrQ9TMgL8/bjvkmas//MIczfudzqOMUV21iltReRm4DNVzc69TlWTRaQpUE9VfyvGe3cB4lQ13v1eXwCDAc+5zQcDT7ufTwXeERFxL/9CVU8A20Ukzn2834uRo0Brv3ial3KW0ysnAr4OhDL9tuiF30zL3bdl5z7PEIValZMI+s4PXRuOOJbkXPlocinr3KX0fmf5HKnpWWzcc4QGN4ylQeOSHTW8oDnRw4GVIrIc11VXSUAI0Ay4GDgAnHHqqZAaALs9XicAXfPbRlWzROSwO1MDYEmufRvk9SYicidwJ0CjRsWblzr46G5iKv1J1cMpcPjUkYt1rGIp83/kheGNmc6BQz9jP6BjaAZ7D5/g2J8HqBJc0H9JL+SzXyjKOHep/ZzOfty0I+mEn8gi1D+rxN/5rP9aVfVNEXkH6IPrlFV74DiwEbhFVXeVeKISpqrjgfEAMTExxfob7DL6E9fYRX7l7Jem8QqVs3MY8cp8IoKC+e6e7ohXfmEwvmjHgWP0eW0+I3s24fHIliV+/AK/7rhPX812P0pSItDQ43Wke1le2yS4b2SsBiQXct8S5W/Fw5SSQHdfyOPT1vLrliR6t6zwY5WaEvLOvDgC/f0Y2TO6VI5f2Mt4a4nI4yIyXkQmnXyc43svA5qLSLSIBOHqFJ+ea5vpwDD382uBX9zDqkwHbnRfpRUNNAeWnmMeYxxz7QWRNKheiTftiixTQnYlpzFtZSJ/7dqY2mEhpfIehb0K63tc3/7nAD96PIpNVbOA+4FZuE6JfaWq60XkWREZ5N5sIhDu7iR/CHd/i6quB77C1eH+E3BfXh39xviKoAA/7undlJW7DvFb3AGn45hy4N15cfj7CXdd3KTU3kMK821HRFapasdSS1FGYmJiNDY21ukYxuTpRFY2vV+ZT4Pqlfj67m7WF2KKbXdKGpe8Op+bL2zM04PanvPxRGS5qsbkXl7YFsgPInLFOacwxuQrOMCfe3o3JXbnQX7flux0HOPD3pu/DT8p3dYHFL6APICriBwXkSMikioiR0ozmDEV0fUxDalTNZg35m51OorxUYmHjjN1+W5u6NyQetUqlep7FXYwxTBV9VPVSqpa1f26aqkmM6YCCgn05+6Lm7J0ewpL4q0VYoru/flxANzdu2RvGsxLQVPatnL/2SmvR6mnM6YCGtqlEbXCgnlzjrVCTNHsOXycr5YlcF1MQxpUL93WBxR8H8hDuO7ifs1jmWeve58ST2RMBRcS6M9dvZrw/I8bWbo9hS7RNZ2OZHzEuPnbyFHlnotLv/UBBU9pe6f76fvAYFW9BJiHa0CPf5RyNmMqrL92bUxElSDesr4QU0j7jqTz+bLdXHtBJA1rhpbJexa2E/0JVT0iIhfhanVMwFVUjDGloFKQP3f2asJvcQdYvjPF6TjGB3zwazzZOcq9vctuyonCFpCTN+ldCXyoqj8CQaUTyRgDcPOFjQmvHMQb1hdiCrA/NZ0pf+zkmvMb0Ci8bFofUPgCkigiHwA3ADNEJLgI+xpjiiE0KIA7ejVh4dYDrNh10Ok4xot9uCCezOwc7r+kbCe8K2wRuB7XkCOXqeohoCbwcGmFMsa43HJhY2qEBlpfiMnXgaMn+GTJTq7u2ICoiMpl+t6FvQ8kTVW/VdWt7td7VPXn0o1mjKkcHMDInk2YvzmJ1bsPOR3HeKEPF8aTkZXDfQ5Mt22noYzxcsO6R1HdWiEmDynHMvjk951c1aE+TWtVKfP3twJijJerEhzAiB7RzN20n7UJhwvewVQYExbGczwzm1EOtD7ACogxPmFYjyiqhgTw1i/WCjEuB49lMHnxDq48rx7Naoc5ksEKiDE+oGpIILdfFM3sDftY/6e1QgxM+C2etMxsRvVp7lgGKyDG+IjbekQTFhJgfSGGlGMZ/HeRq/XRsq4zrQ9wqICISE0RmS0iW91/1shjm44i8ruIrBeRNSJyg8e6j0Rku4iscj86lukHMMYB1SoFcluPaGat38fGPTabQkX2wa/bSM/M5m+XOtf6AOdaIGOAuaraHJjrfp1bGnCrqrYFBgBviEh1j/UPq2pH92NVaQc2xhvc3iOKKsEBvG19IRVWUuoJJv++g8EdGzjW93GSUwVkMDDZ/XwycHXuDVR1i8d9J38C+4FaZRXQGG9UPTSI4d2jmLF2L5v3pjodxzhg3K/byMxWRvd1tvUBzhWQOqq6x/18L1DnbBuLSBdcY29t81j8gvvU1lj30Cr57XuniMSKSGxSUtI5BzfGaSMuiqZKcABjZ29xOoopY/uOpPPpkp385fwGRJfxXed5KbUCIiJzRGRdHo/BntupqnL6HCO5j1MP+AS4TVVz3IsfA1oBnXENq/Jofvur6nhVjVHVmFq1rAFjfF+NykGMuCian9bvtftCKpj35sWRnaOOXnnlqdQKiKpeqqrt8nh8D+xzF4aTBWJ/XscQkarAj8A/VXWJx7H3qMsJ4L9Al9L6HMZ4oxE9o6lWKZDXZ292OoopI4mHjvP50t1cFxNZpiPuno1Tp7CmA8Pcz4cB3+feQESCgGnAx6o6Nde6k8VHcPWfrCvNsMZ4m6ohgdx9cVPmbU6y+UIqiHfnxaEo93tJ6wOcKyAvAf1EZCtwqfs1IhIjIhPc21wP9AKG53G57hQRWQusBSKA58s0vTFeYFh316yFr86yvpDybndKGl8t282NnRuVyVznhVXQnOilQlWTgb55LI8FRrqffwp8ms/+Nhe7qfBCgwK4t3cznv1hA4vjDtC9WYTTkUwpefuXrfj5CfeV8XwfBbE70Y3xYTd1bUS9aiG88vNmXNejmPJmx4FjfLMikb92bUTdaiFOxzmNFRBjfFhIoD+j+jRn5a5DzNuc57Uoxse9NXcrgf7CPb2bOh3lDFZAjPFx18VE0qhmKK/9vIWcHGuFlCeb96YybVUit3aLonaYd7U+wAqIMT4v0N+Pv13anPV/HmHW+r1OxzEl6JVZm6kSHMC9Xtj6ACsgxpQLrnGRqvDa7C1kWyukXFi+8yBzNu7j7oubUj00yOk4ebICYkw54O8nPNSvBXH7jzJ9daLTccw5UlX+89MmIqoEc1uPKKfj5MsKiDHlxIC2dWlTryqvz97Ciaxsp+OYc/DrliSWbk9hdN9mhAY5crdFoVgBMaac8PMTHr28FbtTjvPZH7ucjmOKKSdHefmnzTSsWYkbOzdyOs5ZWQExphzp1TyCHs3CefuXOFLTM52OY4rhh7V72LDnCH/v15KgAO/+Fe3d6YwxRSIijBnQmpRjGXzwa7zTcUwRZWbn8NrPm2lVN4xBHeo7HadAVkCMKWfOi6zGVR3qM+G3ePYdSXc6jimCr2J3szM5jUcGtMTPT5yOUyArIMaUQw/3b0l2jvLGHJv61lekZWTx5pytdI6qwSUtazsdp1CsgBhTDjUKD+WvXRvzVexu4vYfdTqOKYTxC+LZn3qCMZe3wjVThfezAmJMOTWqTzMqBfrz8k+bnI5iCrD/SDof/BrPlefV44LGNZ2OU2hWQIwpp8KrBHNXryb8vGEfsTts0ilv9trPW8jKyeGRAS2djlIkjhQQEakpIrNFZKv7zxr5bJftMZnUdI/l0SLyh4jEiciX7tkLjTG5jOgZTa2wYF6cucmGe/dSG/cc4avluxnWLYrG4ZWdjlMkTrVAxgBzVbU5MNf9Oi/HVbWj+zHIY/l/gLGq2gw4CIwo3bjG+KbQoAAe6teC5TsP8uPaPU7HMXn494yNVA0J5P4+3jVZVGE4VUAGA5Pdzyfjmte8UNzzoPcBTs6TXqT9jaloro9pSOt6VXlxxibSM22IE28yf/N+Fm49wOi+zb12wMSzcaqA1FHVk1+H9gJ18tkuRERiRWSJiFztXhYOHFLVLPfrBKBBfm8kIne6jxGblJRUEtmN8Sn+fsK/BrYm8dBxJiy0mwu9RVZ2Dv+esZHG4aHccmFjp+MUS6kVEBGZIyLr8ngM9txOXSdm8zs521hVY4CbgDdEpMiD4qvqeFWNUdWYWrVqFf2DGFMOdG8awWVt6/De/G12c6GX+Hp5Alv2HWXMgFZeP2RJfkottapeqqrt8nh8D+wTkXoA7j/znItTVRPdf8YD84HzgWSguoicHKIyErDxq40pwONXtCYr2zVQn3HW4bRMXpm1mc5RNRjQrq7TcYrNqbI3HRjmfj4M+D73BiJSQ0SC3c8jgB7ABneLZR5w7dn2N8acrnF4ZW67KIpvViSwevchp+NUaGPnbOFQWgZPD2rrMzcN5sWpAvIS0E9EtgKXul8jIjEiMsG9TWsgVkRW4yoYL6nqBve6R4GHRCQOV5/IxDJNb4yPuv+SZkRUCebZHzbYZb0O2bw3lU+W7OSmro1oW7+a03HOiSMzlahqMtA3j+WxwEj388XAefnsHw90Kc2MxpRHYSGBPHxZCx79Zi3TV//J4I75Xn9iSoGq8vT09YSFBPD3fr5102BefLPnxhhTbNde0JDzGlTjhR832pwhZWzG2r38Hp/M3/u3pEZl37tsNzcrIMZUMP5+wnNXtyPp6AkbrbcMpWVk8cKPG2hdryo3dfHumQYLywqIMRVQx4bVGdqlER8t3sHGPUecjlMhjJu/jT8Pp/PMoLb4+8BcH4VhBcSYCuqRy1pSrVIgT3y3jpwc61AvTduSjjLu13gGdahPl2jfGW23IFZAjKmgqocGMebyVizfeZCpKxKcjlNuqSr/nLaWkEA/nhjY2uk4JcoKiDEV2LWdIolpXIOXZm7iUFqG03HKpW9WJLIkPoUxl7emdliI03FKlBUQYyowP3eH+uHjmfzHJp4qcSnHMnjhxw1c0LgGN3Zu6HScEmcFxJgKrnW9qtzeI4rPl+7m923JTscpV/49YyOp6Vn8+5rz8CsnHeeerIAYY3ioX0sah4cy5ts1HM+wId9Lwu/bkpm6PIE7ezWhZd0wp+OUCisgxhgqBfnz4l/OY2dyGmPnbHE6js87npHN49PW0qhmKKP6NHc6TqmxAmKMAVxDvg/t0ogJC+NtsMVz9MqszWw/cIyXhpxHpSB/p+OUGisgxphTHruiFbXDQnhk6hoysnKcjuOTlm5P4b+Lt3PLhY3p3jTC6TilygqIMeaUqiGBvHBNOzbvS+XdeXFOx/E5aRlZPDJ1NZE1KjHm8lZOxyl1VkCMMafp27oO15zfgHfmxbHKTmUVycs/bWZHchovD+lA5WBHBjsvU1ZAjDFneHpQW+qEBfPgl6tIy8hyOo5PWBKfzEeLdzCsW2O6NQ13Ok6ZsAJijDlDtUqBvHZ9R3YkH+OFHzc6HcfrHU7L5KEvVxEVHsqjFeDU1UmOFBARqSkis0Vkq/vPGnlsc4mIrPJ4pIvI1e51H4nIdo91Hcv6MxhT3nVrGs4dPZsw5Y9d/LJpn9NxvJaq8ti0NexPPcGbN55PaFD5P3V1klMtkDHAXFVtDsx1vz6Nqs5T1Y6q2hHoA6QBP3ts8vDJ9aq6qgwyG1Ph/L1/C1rVDeORqWs4cPSE03G80texCcxYu5e/929Jh4bVnY5TppwqIIOBye7nk4GrC9j+WmCmqqaVZihjzOmCA/x588bzSU3P4sEvV5Ftw76fZlvSUZ6avp7uTcO5q1cTp+OUOacKSB1V3eN+vheoU8D2NwKf51r2goisEZGxIhKc344icqeIxIpIbFJS0jlENqZialk3jGcGtWXh1gN2aa+H9MxsRn22kuBAP16/vmO5HOuqIKVWQERkjoisy+Mx2HM7VVUg3681IlIPOA+Y5bH4MaAV0BmoCTya3/6qOl5VY1Q1platWufykYypsG7o3JC/nN+AsXO2sCjugNNxHKeqPPHdOjbsOcJr13WgbrXyNUx7YZVaAVHVS1W1XR6P74F97sJwskDsP8uhrgemqWqmx7H3qMsJ4L9Al9L6HMYYEBGev6YdzWpV4YEvVrL/SLrTkRz1+dLdTF2ewOg+zejbuqATKOWXU6ewpgPD3M+HAd+fZduh5Dp95VF8BFf/ybqSj2iM8RQaFMB7f+3EsRPZ3DtlBSeyKuaovat3H+Lp6evp2TyCBy5t4XQcRzlVQF4C+onIVuBS92tEJEZEJpzcSESigIbAr7n2nyIia4G1QATwfFmENqaia14njFeua0/szoM8MW0drjPQFceBoye4d8oKaoUF89aN5+NfAfs9PDlywbKqJgN981geC4z0eL0DaJDHdn1KM58xJn8D29dny76jvDV3Ky3rhjGyZ8W4+ig9M5s7P44l+dgJvrqrGzUqBzkdyXF2J7oxpsj+1rc5l7ery79nbGTe5rN1YZYPqsrDU9ewYtchxl7fkfaR1Z2O5BWsgBhjiszPT3jt+g60qluV0Z+tZP2fh52OVKrGztnK/1b/ySMDWnL5efWcjuM1rIAYY4olNCiAicNjqBISwLBJy9iVXD7v8/186S7emruV6y6I5J6Lmzodx6tYATHGFFu9apX4ZEQXsnJyuGXSHySllq/hTn5Y8yePT1tL75a1eOGa83Bd+GlOsgJijDknzWqHMWl4Z/YfOcHw/y7lcFpmwTv5gHmb9/Pgl6vo3Lgm7//1AoIC7NdlbvYTMcacs06NavD+zZ3Yuu8of524hENpGU5HOieL4w5wz6fLaVEnjAnDY8r1vObnwgqIMaZE9G5Zmw9uuYAt+45y04d/kHLMN4vIvE37Gf7RMhrXrMzk27tQNSTQ6UheywqIMabEXNKqNh/eGsO2pKPc9OESnxvy5Kd1e7nzk1ha1KnCF3deSESVfMdpNVgBMcaUsItb1GLisM7sSknjmvcWs3VfqtORCuWT33dw75TlnNegGlNGXmg3ChaCFRBjTIm7qHkEX97ZjRNZOQx5fzG/b0t2OlK+snOU537YwL++X0+fVrX5ZERXqlWy01aFYQXEGFMqzousxrR7u1MrLJhbJv7BpN+2e93YWYfSMrjj41gm/rad4d2j+OCWGCoHV5wpac+VFRBjTKlpWDOUb+/tQe+WtXn2hw3c//lKjp7IcjoWACt3HeTKt35j4dYknh3clqcHta3wgyMWlRUQY0ypqlYpkPG3XMCjA1oxc+0eLn9zgaOntDKzc3h3XhzXf/A7AF/f3Z1bu0U5lseXWQExxpQ6Pz/hnt5N+fKubviJMPTDJTz5/TpS08v2psN1iYe5+t1FvDJrM/3b1GXG6J50bFi9TDOUJ+Jt5yRLU0xMjMbGxjodw5gK7XhGNq/M2sx/F28nvHIQf7u0BTd2bkiAf+l9n913JJ3Xft7M1OUJ1KwczPNXt2VAOxsUsbBEZLmqxpyx3AqIMcYJaxIO8fyPG1m6PYXoiMrc2asJ15zfgJDAkrvre3dKGpMWbeeLpbvJyslhWLcoRvVpTrVQu8qqKLyqgIjIdcDTQGugi3siqby2GwC8CfgDE1T15MyF0cAXQDiwHLhFVQu87dUKiDHeRVX5ecM+3vkljrWJh4moEsw159fnmvMjaV0vrFiDF6ZnZjN/cxLfrUxk9sZ9CDCoQ33+dmkLGoWHlvyHqAC8rYC0BnKAD4B/5FVARMQf2AL0AxKAZcBQVd0gIl8B36rqFyIyDlitqu8X9L5WQIzxTqrK79uSmbRoB/M37ycrR2lUM5QezcLpGh1OizphNKlV+YzWiaqSfCyD+KRjrEk4xJL4ZP6ITyH1RBYRVYIY0imS4T2iqFetkkOfrHzIr4A4NaXtRqCgbxddgDhVjXdv+wUwWEQ2An2Am9zbTcbVmimwgBhjvJOI0L1ZBN2bRZByLIMf1+7h181J/LB6D58v3e3eBsKCAwgLCSTQX0jLyOboiSzSMrJPHScqPJSBHepxebt6dG8aXqr9KsahAlJIDYDdHq8TgK64TlsdUtUsj+VnzJt+kojcCdwJ0KhRo9JJaowpMTUrB3HLhY255cLGZGXnsHX/UbYlHWXb/mMcTMsgNT2LjOwcKgf5Uzk4gMgalYiOqEyrulWpWy3E6fgVSqkVEBGZA9TNY9U/VfX70nrf3FR1PDAeXKewyup9jTHnLsDfj9b1qtK6XlWno5g8lFoBUdVLz/EQiUBDj9eR7mXJQHURCXC3Qk4uN8YYU4a8+QThMqC5iESLSBBwIzBdXb3+84Br3dsNA8qsRWOMMcbFkQIiIteISALQDfhRRGa5l9cXkRkA7tbF/cAsYCPwlaqudx/iUeAhEYnD1Scysaw/gzHGVHR2I6Exxpizyu8yXm8+hWWMMcaLWQExxhhTLFZAjDHGFIsVEGOMMcVSoTrRRSQJ2FnM3SOAAyUYp6z5en7w/c/g6/nB9z+Dr+cHZz5DY1WtlXthhSog50JEYvO6CsFX+Hp+8P3P4Ov5wfc/g6/nB+/6DHYKyxhjTLFYATHGGFMsVkAKb7zTAc6Rr+cH3/8Mvp4ffP8z+Hp+8KLPYH0gxhhjisVaIMYYY4rFCogxxphisQJSCCIyQEQ2i0iciIxxOk9RiMgkEdkvIuuczlIcItJQROaJyAYRWS8iDzidqahEJERElorIavdneMbpTMUhIv4islJEfnA6S3GIyA4RWSsiq0TE50ZVFZHqIjJVRDaJyEYR6eZ4JusDOTsR8Qe2AP1wTZ+7DBiqqhscDVZIItILOAp8rKrtnM5TVCJSD6inqitEJAxYDlztKz9/ABERoLKqHhWRQOA34AFVXeJwtCIRkYeAGKCqqg50Ok9RicgOIEZVffJGQhGZDCxU1QnuOZJCVfWQk5msBVKwLkCcqsaragbwBTDY4UyFpqoLgBSncxSXqu5R1RXu56m45oZp4GyqolGXo+6Xge6HT31zE5FI4EpggtNZKiIRqQb0wj33kapmOF08wApIYTQAdnu8TsDHfoGVFyISBZwP/OFwlCJzn/5ZBewHZquqr32GN4BHgByHc5wLBX4WkeUicqfTYYooGkgC/us+jThBRCo7HcoKiPEJIlIF+Ab4m6oecTpPUalqtqp2BCKBLiLiM6cTRWQgsF9Vlzud5RxdpKqdgMuB+9ynd31FANAJeF9VzweOAY73x1oBKVgi0NDjdaR7mSkj7n6Db4Apqvqt03nOhfu0wzxggMNRiqIHMMjdh/AF0EdEPnU2UtGpaqL7z/3ANFynp31FApDg0XKdiqugOMoKSMGWAc1FJNrdcXUjMN3hTBWGuwN6IrBRVV93Ok9xiEgtEanufl4J1wUZmxwNVQSq+piqRqpqFK5//7+o6s0OxyoSEansvggD96mf/oDPXJmoqnuB3SLS0r2oL+D4hSQBTgfwdqqaJSL3A7MAf2CSqq53OFahicjnQG8gQkQSgKdUdaKzqYqkB3ALsNbdhwDwuKrOcC5SkdUDJruv6PMDvlJVn7wU1ofVAaa5vo8QAHymqj85G6nIRgFT3F9k44HbHM5jl/EaY4wpHjuFZYwxplisgBhjjCkWKyDGGGOKxQqIMcaYYrECYowxplisgBhjjCkWKyDGGGOKxQqIMQ4Skc4issY9Z0hl93whPjNOlqnY7EZCYxwmIs8DIUAlXOMdvehwJGMKxQqIMQ5zD02xDEgHuqtqtsORjCkUO4VljPPCgSpAGK6WiDE+wVogxjhMRKbjGiY9Gtf0vfc7HMmYQrHReI1xkIjcCmSq6mfu0XoXi0gfVf3F6WzGFMRaIMYYY4rF+kCMMcYUixUQY4wxxWIFxBhjTLFYATHGGFMsVkCMMcYUixUQY4wxxWIFxBhjTLH8H9QqPB/Cq0OLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Test the model\n",
    "x_test = np.linspace(0, 2*np.pi, 100)\n",
    "x_test_tensor = torch.tensor(x_test.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "with torch.no_grad():\n",
    "    y_pred = model(x_test_tensor)\n",
    "\n",
    "# Plot the true sin(x) and predicted sin(x)\n",
    "plt.plot(x, y, label='True sin(x)')\n",
    "plt.plot(x_test, y_pred.squeeze().numpy(), label='Predicted sin(x)')\n",
    "plt.legend()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "plt.title('sin(x) Approximation')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "459f2982",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/localhome/tkapoor/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([100, 1, 1])) that is different to the input size (torch.Size([100, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/500], Loss: 0.4975\n",
      "Epoch [20/500], Loss: 0.4968\n",
      "Epoch [30/500], Loss: 0.4954\n",
      "Epoch [40/500], Loss: 0.4952\n",
      "Epoch [50/500], Loss: 0.4951\n",
      "Epoch [60/500], Loss: 0.4950\n",
      "Epoch [70/500], Loss: 0.4950\n",
      "Epoch [80/500], Loss: 0.4950\n",
      "Epoch [90/500], Loss: 0.4950\n",
      "Epoch [100/500], Loss: 0.4950\n",
      "Epoch [110/500], Loss: 0.4950\n",
      "Epoch [120/500], Loss: 0.4950\n",
      "Epoch [130/500], Loss: 0.4950\n",
      "Epoch [140/500], Loss: 0.4950\n",
      "Epoch [150/500], Loss: 0.4950\n",
      "Epoch [160/500], Loss: 0.4950\n",
      "Epoch [170/500], Loss: 0.4950\n",
      "Epoch [180/500], Loss: 0.4950\n",
      "Epoch [190/500], Loss: 0.4950\n",
      "Epoch [200/500], Loss: 0.4950\n",
      "Epoch [210/500], Loss: 0.4950\n",
      "Epoch [220/500], Loss: 0.4950\n",
      "Epoch [230/500], Loss: 0.4950\n",
      "Epoch [240/500], Loss: 0.4950\n",
      "Epoch [250/500], Loss: 0.4950\n",
      "Epoch [260/500], Loss: 0.4950\n",
      "Epoch [270/500], Loss: 0.4950\n",
      "Epoch [280/500], Loss: 0.4950\n",
      "Epoch [290/500], Loss: 0.4950\n",
      "Epoch [300/500], Loss: 0.4950\n",
      "Epoch [310/500], Loss: 0.4950\n",
      "Epoch [320/500], Loss: 0.4950\n",
      "Epoch [330/500], Loss: 0.4950\n",
      "Epoch [340/500], Loss: 0.4950\n",
      "Epoch [350/500], Loss: 0.4950\n",
      "Epoch [360/500], Loss: 0.4950\n",
      "Epoch [370/500], Loss: 0.4950\n",
      "Epoch [380/500], Loss: 0.4950\n",
      "Epoch [390/500], Loss: 0.4950\n",
      "Epoch [400/500], Loss: 0.4950\n",
      "Epoch [410/500], Loss: 0.4950\n",
      "Epoch [420/500], Loss: 0.4950\n",
      "Epoch [430/500], Loss: 0.4950\n",
      "Epoch [440/500], Loss: 0.4950\n",
      "Epoch [450/500], Loss: 0.4950\n",
      "Epoch [460/500], Loss: 0.4950\n",
      "Epoch [470/500], Loss: 0.4950\n",
      "Epoch [480/500], Loss: 0.4950\n",
      "Epoch [490/500], Loss: 0.4950\n",
      "Epoch [500/500], Loss: 0.4950\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate training data\n",
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x_tensor = torch.tensor(x.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "y_tensor = torch.tensor(y.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "\n",
    "# Define the RNN model\n",
    "class RNNSin(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(RNNSin, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "        \n",
    "    def forward(self, input):\n",
    "        batch_size = input.size(0)\n",
    "        hidden = self.init_hidden(batch_size)\n",
    "        output, _ = self.rnn(input, hidden)\n",
    "        output = self.fc(output[:, -1, :])\n",
    "        return output\n",
    "    \n",
    "    def init_hidden(self, batch_size):\n",
    "        hidden = torch.zeros(1, batch_size, self.hidden_size)\n",
    "        return hidden\n",
    "\n",
    "# Set hyperparameters\n",
    "input_size = 1\n",
    "hidden_size = 32\n",
    "output_size = 1\n",
    "learning_rate = 0.01\n",
    "num_epochs = 500\n",
    "\n",
    "# Initialize the RNN model\n",
    "model = RNNSin(input_size, hidden_size, output_size)\n",
    "\n",
    "# Loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training loop\n",
    "losses = []\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass\n",
    "    output = model(x_tensor)\n",
    "    loss = criterion(output, y_tensor)\n",
    "    \n",
    "    # Backward pass and optimization\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    # Print progress\n",
    "    if (epoch+1) % 10 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "    \n",
    "    # Store the loss\n",
    "    losses.append(loss.item())\n",
    "\n",
    "# Plot the training loss\n",
    "plt.plot(losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss')\n",
    "plt.show()\n",
    "\n",
    "# Test the model\n",
    "x_test = np.linspace(0, 2*np.pi, 200)\n",
    "x_test_tensor = torch.tensor(x_test.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "with torch.no_grad():\n",
    "    y_pred = model(x_test_tensor)\n",
    "\n",
    "# Plot the true sin(x) and predicted sin(x)\n",
    "plt.plot(x, y, label='True sin(x)')\n",
    "plt.plot(x_test, y_pred.squeeze().numpy(), label='Predicted sin(x)')\n",
    "plt.legend()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "plt.title('sin(x) Approximation')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "57981502",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/localhome/tkapoor/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([100, 1, 1])) that is different to the input size (torch.Size([100, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/500], Loss: 0.4960\n",
      "Epoch [20/500], Loss: 0.4982\n",
      "Epoch [30/500], Loss: 0.4981\n",
      "Epoch [40/500], Loss: 0.4966\n",
      "Epoch [50/500], Loss: 0.4955\n",
      "Epoch [60/500], Loss: 0.4951\n",
      "Epoch [70/500], Loss: 0.4951\n",
      "Epoch [80/500], Loss: 0.4950\n",
      "Epoch [90/500], Loss: 0.4950\n",
      "Epoch [100/500], Loss: 0.4950\n",
      "Epoch [110/500], Loss: 0.4950\n",
      "Epoch [120/500], Loss: 0.4950\n",
      "Epoch [130/500], Loss: 0.4950\n",
      "Epoch [140/500], Loss: 0.4950\n",
      "Epoch [150/500], Loss: 0.4950\n",
      "Epoch [160/500], Loss: 0.4950\n",
      "Epoch [170/500], Loss: 0.4950\n",
      "Epoch [180/500], Loss: 0.4950\n",
      "Epoch [190/500], Loss: 0.4950\n",
      "Epoch [200/500], Loss: 0.4950\n",
      "Epoch [210/500], Loss: 0.4950\n",
      "Epoch [220/500], Loss: 0.4950\n",
      "Epoch [230/500], Loss: 0.4950\n",
      "Epoch [240/500], Loss: 0.4950\n",
      "Epoch [250/500], Loss: 0.4950\n",
      "Epoch [260/500], Loss: 0.4950\n",
      "Epoch [270/500], Loss: 0.4950\n",
      "Epoch [280/500], Loss: 0.4950\n",
      "Epoch [290/500], Loss: 0.4950\n",
      "Epoch [300/500], Loss: 0.4950\n",
      "Epoch [310/500], Loss: 0.4950\n",
      "Epoch [320/500], Loss: 0.4950\n",
      "Epoch [330/500], Loss: 0.4950\n",
      "Epoch [340/500], Loss: 0.4950\n",
      "Epoch [350/500], Loss: 0.4950\n",
      "Epoch [360/500], Loss: 0.4950\n",
      "Epoch [370/500], Loss: 0.4950\n",
      "Epoch [380/500], Loss: 0.4950\n",
      "Epoch [390/500], Loss: 0.4950\n",
      "Epoch [400/500], Loss: 0.4950\n",
      "Epoch [410/500], Loss: 0.4950\n",
      "Epoch [420/500], Loss: 0.4950\n",
      "Epoch [430/500], Loss: 0.4950\n",
      "Epoch [440/500], Loss: 0.4950\n",
      "Epoch [450/500], Loss: 0.4950\n",
      "Epoch [460/500], Loss: 0.4950\n",
      "Epoch [470/500], Loss: 0.4950\n",
      "Epoch [480/500], Loss: 0.4950\n",
      "Epoch [490/500], Loss: 0.4950\n",
      "Epoch [500/500], Loss: 0.4950\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate training data\n",
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Scale the input data\n",
    "x_scaled = (x - np.pi) / np.pi * 10\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x_tensor = torch.tensor(x_scaled.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "y_tensor = torch.tensor(y.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "\n",
    "# Define the RNN model\n",
    "class RNNSin(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(RNNSin, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "        \n",
    "    def forward(self, input):\n",
    "        batch_size = input.size(0)\n",
    "        hidden = self.init_hidden(batch_size)\n",
    "        output, _ = self.rnn(input, hidden)\n",
    "        output = self.fc(output[:, -1, :])\n",
    "        return output\n",
    "    \n",
    "    def init_hidden(self, batch_size):\n",
    "        hidden = torch.zeros(1, batch_size, self.hidden_size)\n",
    "        return hidden\n",
    "\n",
    "# Set hyperparameters\n",
    "input_size = 1\n",
    "hidden_size = 32\n",
    "output_size = 1\n",
    "learning_rate = 0.01\n",
    "num_epochs = 500\n",
    "\n",
    "# Initialize the RNN model\n",
    "model = RNNSin(input_size, hidden_size, output_size)\n",
    "\n",
    "# Loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# Training loop\n",
    "losses = []\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass\n",
    "    output = model(x_tensor)\n",
    "    loss = criterion(output, y_tensor)\n",
    "    \n",
    "    # Backward pass and optimization\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    # Print progress\n",
    "    if (epoch+1) % 10 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "    \n",
    "    # Store the loss\n",
    "    losses.append(loss.item())\n",
    "\n",
    "# Plot the training loss\n",
    "plt.plot(losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss')\n",
    "plt.show()\n",
    "\n",
    "# Test the model\n",
    "x_test = np.linspace(0, 2*np.pi, 200)\n",
    "x_test_scaled = (x_test - np.pi) / np.pi * 10\n",
    "x_test_tensor = torch.tensor(x_test_scaled.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "with torch.no_grad():\n",
    "    y_pred = model(x_test_tensor)\n",
    "\n",
    "# Plot the true sin(x) and predicted sin(x)\n",
    "plt.plot(x, y, label='True sin(x)')\n",
    "plt.plot(x_test, y_pred.squeeze().numpy(), label='Predicted sin(x)')\n",
    "plt.legend()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "plt.title('sin(x) Approximation')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2d178ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/localhome/tkapoor/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([100, 1, 1])) that is different to the input size (torch.Size([100, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/5000], Loss: 0.4989\n",
      "Epoch [20/5000], Loss: 0.4986\n",
      "Epoch [30/5000], Loss: 0.4982\n",
      "Epoch [40/5000], Loss: 0.4980\n",
      "Epoch [50/5000], Loss: 0.4977\n",
      "Epoch [60/5000], Loss: 0.4974\n",
      "Epoch [70/5000], Loss: 0.4972\n",
      "Epoch [80/5000], Loss: 0.4970\n",
      "Epoch [90/5000], Loss: 0.4968\n",
      "Epoch [100/5000], Loss: 0.4967\n",
      "Epoch [110/5000], Loss: 0.4965\n",
      "Epoch [120/5000], Loss: 0.4964\n",
      "Epoch [130/5000], Loss: 0.4963\n",
      "Epoch [140/5000], Loss: 0.4962\n",
      "Epoch [150/5000], Loss: 0.4961\n",
      "Epoch [160/5000], Loss: 0.4960\n",
      "Epoch [170/5000], Loss: 0.4959\n",
      "Epoch [180/5000], Loss: 0.4958\n",
      "Epoch [190/5000], Loss: 0.4957\n",
      "Epoch [200/5000], Loss: 0.4957\n",
      "Epoch [210/5000], Loss: 0.4956\n",
      "Epoch [220/5000], Loss: 0.4955\n",
      "Epoch [230/5000], Loss: 0.4955\n",
      "Epoch [240/5000], Loss: 0.4955\n",
      "Epoch [250/5000], Loss: 0.4954\n",
      "Epoch [260/5000], Loss: 0.4954\n",
      "Epoch [270/5000], Loss: 0.4953\n",
      "Epoch [280/5000], Loss: 0.4953\n",
      "Epoch [290/5000], Loss: 0.4953\n",
      "Epoch [300/5000], Loss: 0.4953\n",
      "Epoch [310/5000], Loss: 0.4952\n",
      "Epoch [320/5000], Loss: 0.4952\n",
      "Epoch [330/5000], Loss: 0.4952\n",
      "Epoch [340/5000], Loss: 0.4952\n",
      "Epoch [350/5000], Loss: 0.4952\n",
      "Epoch [360/5000], Loss: 0.4951\n",
      "Epoch [370/5000], Loss: 0.4951\n",
      "Epoch [380/5000], Loss: 0.4951\n",
      "Epoch [390/5000], Loss: 0.4951\n",
      "Epoch [400/5000], Loss: 0.4951\n",
      "Epoch [410/5000], Loss: 0.4951\n",
      "Epoch [420/5000], Loss: 0.4951\n",
      "Epoch [430/5000], Loss: 0.4951\n",
      "Epoch [440/5000], Loss: 0.4951\n",
      "Epoch [450/5000], Loss: 0.4951\n",
      "Epoch [460/5000], Loss: 0.4951\n",
      "Epoch [470/5000], Loss: 0.4951\n",
      "Epoch [480/5000], Loss: 0.4951\n",
      "Epoch [490/5000], Loss: 0.4950\n",
      "Epoch [500/5000], Loss: 0.4950\n",
      "Epoch [510/5000], Loss: 0.4950\n",
      "Epoch [520/5000], Loss: 0.4950\n",
      "Epoch [530/5000], Loss: 0.4950\n",
      "Epoch [540/5000], Loss: 0.4950\n",
      "Epoch [550/5000], Loss: 0.4950\n",
      "Epoch [560/5000], Loss: 0.4950\n",
      "Epoch [570/5000], Loss: 0.4950\n",
      "Epoch [580/5000], Loss: 0.4950\n",
      "Epoch [590/5000], Loss: 0.4950\n",
      "Epoch [600/5000], Loss: 0.4950\n",
      "Epoch [610/5000], Loss: 0.4950\n",
      "Epoch [620/5000], Loss: 0.4950\n",
      "Epoch [630/5000], Loss: 0.4950\n",
      "Epoch [640/5000], Loss: 0.4950\n",
      "Epoch [650/5000], Loss: 0.4950\n",
      "Epoch [660/5000], Loss: 0.4950\n",
      "Epoch [670/5000], Loss: 0.4950\n",
      "Epoch [680/5000], Loss: 0.4950\n",
      "Epoch [690/5000], Loss: 0.4950\n",
      "Epoch [700/5000], Loss: 0.4950\n",
      "Epoch [710/5000], Loss: 0.4950\n",
      "Epoch [720/5000], Loss: 0.4950\n",
      "Epoch [730/5000], Loss: 0.4950\n",
      "Epoch [740/5000], Loss: 0.4950\n",
      "Epoch [750/5000], Loss: 0.4950\n",
      "Epoch [760/5000], Loss: 0.4950\n",
      "Epoch [770/5000], Loss: 0.4950\n",
      "Epoch [780/5000], Loss: 0.4950\n",
      "Epoch [790/5000], Loss: 0.4950\n",
      "Epoch [800/5000], Loss: 0.4950\n",
      "Epoch [810/5000], Loss: 0.4950\n",
      "Epoch [820/5000], Loss: 0.4950\n",
      "Epoch [830/5000], Loss: 0.4950\n",
      "Epoch [840/5000], Loss: 0.4950\n",
      "Epoch [850/5000], Loss: 0.4950\n",
      "Epoch [860/5000], Loss: 0.4950\n",
      "Epoch [870/5000], Loss: 0.4950\n",
      "Epoch [880/5000], Loss: 0.4950\n",
      "Epoch [890/5000], Loss: 0.4950\n",
      "Epoch [900/5000], Loss: 0.4950\n",
      "Epoch [910/5000], Loss: 0.4950\n",
      "Epoch [920/5000], Loss: 0.4950\n",
      "Epoch [930/5000], Loss: 0.4950\n",
      "Epoch [940/5000], Loss: 0.4950\n",
      "Epoch [950/5000], Loss: 0.4950\n",
      "Epoch [960/5000], Loss: 0.4950\n",
      "Epoch [970/5000], Loss: 0.4950\n",
      "Epoch [980/5000], Loss: 0.4950\n",
      "Epoch [990/5000], Loss: 0.4950\n",
      "Epoch [1000/5000], Loss: 0.4950\n",
      "Epoch [1010/5000], Loss: 0.4950\n",
      "Epoch [1020/5000], Loss: 0.4950\n",
      "Epoch [1030/5000], Loss: 0.4950\n",
      "Epoch [1040/5000], Loss: 0.4950\n",
      "Epoch [1050/5000], Loss: 0.4950\n",
      "Epoch [1060/5000], Loss: 0.4950\n",
      "Epoch [1070/5000], Loss: 0.4950\n",
      "Epoch [1080/5000], Loss: 0.4950\n",
      "Epoch [1090/5000], Loss: 0.4950\n",
      "Epoch [1100/5000], Loss: 0.4950\n",
      "Epoch [1110/5000], Loss: 0.4950\n",
      "Epoch [1120/5000], Loss: 0.4950\n",
      "Epoch [1130/5000], Loss: 0.4950\n",
      "Epoch [1140/5000], Loss: 0.4950\n",
      "Epoch [1150/5000], Loss: 0.4950\n",
      "Epoch [1160/5000], Loss: 0.4950\n",
      "Epoch [1170/5000], Loss: 0.4950\n",
      "Epoch [1180/5000], Loss: 0.4950\n",
      "Epoch [1190/5000], Loss: 0.4950\n",
      "Epoch [1200/5000], Loss: 0.4950\n",
      "Epoch [1210/5000], Loss: 0.4950\n",
      "Epoch [1220/5000], Loss: 0.4950\n",
      "Epoch [1230/5000], Loss: 0.4950\n",
      "Epoch [1240/5000], Loss: 0.4950\n",
      "Epoch [1250/5000], Loss: 0.4950\n",
      "Epoch [1260/5000], Loss: 0.4950\n",
      "Epoch [1270/5000], Loss: 0.4950\n",
      "Epoch [1280/5000], Loss: 0.4950\n",
      "Epoch [1290/5000], Loss: 0.4950\n",
      "Epoch [1300/5000], Loss: 0.4950\n",
      "Epoch [1310/5000], Loss: 0.4950\n",
      "Epoch [1320/5000], Loss: 0.4950\n",
      "Epoch [1330/5000], Loss: 0.4950\n",
      "Epoch [1340/5000], Loss: 0.4950\n",
      "Epoch [1350/5000], Loss: 0.4950\n",
      "Epoch [1360/5000], Loss: 0.4950\n",
      "Epoch [1370/5000], Loss: 0.4950\n",
      "Epoch [1380/5000], Loss: 0.4950\n",
      "Epoch [1390/5000], Loss: 0.4950\n",
      "Epoch [1400/5000], Loss: 0.4950\n",
      "Epoch [1410/5000], Loss: 0.4950\n",
      "Epoch [1420/5000], Loss: 0.4950\n",
      "Epoch [1430/5000], Loss: 0.4950\n",
      "Epoch [1440/5000], Loss: 0.4950\n",
      "Epoch [1450/5000], Loss: 0.4950\n",
      "Epoch [1460/5000], Loss: 0.4950\n",
      "Epoch [1470/5000], Loss: 0.4950\n",
      "Epoch [1480/5000], Loss: 0.4950\n",
      "Epoch [1490/5000], Loss: 0.4950\n",
      "Epoch [1500/5000], Loss: 0.4950\n",
      "Epoch [1510/5000], Loss: 0.4950\n",
      "Epoch [1520/5000], Loss: 0.4950\n",
      "Epoch [1530/5000], Loss: 0.4950\n",
      "Epoch [1540/5000], Loss: 0.4950\n",
      "Epoch [1550/5000], Loss: 0.4950\n",
      "Epoch [1560/5000], Loss: 0.4950\n",
      "Epoch [1570/5000], Loss: 0.4950\n",
      "Epoch [1580/5000], Loss: 0.4950\n",
      "Epoch [1590/5000], Loss: 0.4950\n",
      "Epoch [1600/5000], Loss: 0.4950\n",
      "Epoch [1610/5000], Loss: 0.4950\n",
      "Epoch [1620/5000], Loss: 0.4950\n",
      "Epoch [1630/5000], Loss: 0.4950\n",
      "Epoch [1640/5000], Loss: 0.4950\n",
      "Epoch [1650/5000], Loss: 0.4950\n",
      "Epoch [1660/5000], Loss: 0.4950\n",
      "Epoch [1670/5000], Loss: 0.4950\n",
      "Epoch [1680/5000], Loss: 0.4950\n",
      "Epoch [1690/5000], Loss: 0.4950\n",
      "Epoch [1700/5000], Loss: 0.4950\n",
      "Epoch [1710/5000], Loss: 0.4950\n",
      "Epoch [1720/5000], Loss: 0.4950\n",
      "Epoch [1730/5000], Loss: 0.4950\n",
      "Epoch [1740/5000], Loss: 0.4950\n",
      "Epoch [1750/5000], Loss: 0.4950\n",
      "Epoch [1760/5000], Loss: 0.4950\n",
      "Epoch [1770/5000], Loss: 0.4950\n",
      "Epoch [1780/5000], Loss: 0.4950\n",
      "Epoch [1790/5000], Loss: 0.4950\n",
      "Epoch [1800/5000], Loss: 0.4950\n",
      "Epoch [1810/5000], Loss: 0.4950\n",
      "Epoch [1820/5000], Loss: 0.4950\n",
      "Epoch [1830/5000], Loss: 0.4950\n",
      "Epoch [1840/5000], Loss: 0.4950\n",
      "Epoch [1850/5000], Loss: 0.4950\n",
      "Epoch [1860/5000], Loss: 0.4950\n",
      "Epoch [1870/5000], Loss: 0.4950\n",
      "Epoch [1880/5000], Loss: 0.4950\n",
      "Epoch [1890/5000], Loss: 0.4950\n",
      "Epoch [1900/5000], Loss: 0.4950\n",
      "Epoch [1910/5000], Loss: 0.4950\n",
      "Epoch [1920/5000], Loss: 0.4950\n",
      "Epoch [1930/5000], Loss: 0.4950\n",
      "Epoch [1940/5000], Loss: 0.4950\n",
      "Epoch [1950/5000], Loss: 0.4950\n",
      "Epoch [1960/5000], Loss: 0.4950\n",
      "Epoch [1970/5000], Loss: 0.4950\n",
      "Epoch [1980/5000], Loss: 0.4950\n",
      "Epoch [1990/5000], Loss: 0.4950\n",
      "Epoch [2000/5000], Loss: 0.4950\n",
      "Epoch [2010/5000], Loss: 0.4950\n",
      "Epoch [2020/5000], Loss: 0.4950\n",
      "Epoch [2030/5000], Loss: 0.4950\n",
      "Epoch [2040/5000], Loss: 0.4950\n",
      "Epoch [2050/5000], Loss: 0.4950\n",
      "Epoch [2060/5000], Loss: 0.4950\n",
      "Epoch [2070/5000], Loss: 0.4950\n",
      "Epoch [2080/5000], Loss: 0.4950\n",
      "Epoch [2090/5000], Loss: 0.4950\n",
      "Epoch [2100/5000], Loss: 0.4950\n",
      "Epoch [2110/5000], Loss: 0.4950\n",
      "Epoch [2120/5000], Loss: 0.4950\n",
      "Epoch [2130/5000], Loss: 0.4950\n",
      "Epoch [2140/5000], Loss: 0.4950\n",
      "Epoch [2150/5000], Loss: 0.4950\n",
      "Epoch [2160/5000], Loss: 0.4950\n",
      "Epoch [2170/5000], Loss: 0.4950\n",
      "Epoch [2180/5000], Loss: 0.4950\n",
      "Epoch [2190/5000], Loss: 0.4950\n",
      "Epoch [2200/5000], Loss: 0.4950\n",
      "Epoch [2210/5000], Loss: 0.4950\n",
      "Epoch [2220/5000], Loss: 0.4950\n",
      "Epoch [2230/5000], Loss: 0.4950\n",
      "Epoch [2240/5000], Loss: 0.4950\n",
      "Epoch [2250/5000], Loss: 0.4950\n",
      "Epoch [2260/5000], Loss: 0.4950\n",
      "Epoch [2270/5000], Loss: 0.4950\n",
      "Epoch [2280/5000], Loss: 0.4950\n",
      "Epoch [2290/5000], Loss: 0.4950\n",
      "Epoch [2300/5000], Loss: 0.4950\n",
      "Epoch [2310/5000], Loss: 0.4950\n",
      "Epoch [2320/5000], Loss: 0.4950\n",
      "Epoch [2330/5000], Loss: 0.4950\n",
      "Epoch [2340/5000], Loss: 0.4950\n",
      "Epoch [2350/5000], Loss: 0.4950\n",
      "Epoch [2360/5000], Loss: 0.4950\n",
      "Epoch [2370/5000], Loss: 0.4950\n",
      "Epoch [2380/5000], Loss: 0.4950\n",
      "Epoch [2390/5000], Loss: 0.4950\n",
      "Epoch [2400/5000], Loss: 0.4950\n",
      "Epoch [2410/5000], Loss: 0.4950\n",
      "Epoch [2420/5000], Loss: 0.4950\n",
      "Epoch [2430/5000], Loss: 0.4950\n",
      "Epoch [2440/5000], Loss: 0.4950\n",
      "Epoch [2450/5000], Loss: 0.4950\n",
      "Epoch [2460/5000], Loss: 0.4950\n",
      "Epoch [2470/5000], Loss: 0.4950\n",
      "Epoch [2480/5000], Loss: 0.4950\n",
      "Epoch [2490/5000], Loss: 0.4950\n",
      "Epoch [2500/5000], Loss: 0.4950\n",
      "Epoch [2510/5000], Loss: 0.4950\n",
      "Epoch [2520/5000], Loss: 0.4950\n",
      "Epoch [2530/5000], Loss: 0.4950\n",
      "Epoch [2540/5000], Loss: 0.4950\n",
      "Epoch [2550/5000], Loss: 0.4950\n",
      "Epoch [2560/5000], Loss: 0.4950\n",
      "Epoch [2570/5000], Loss: 0.4950\n",
      "Epoch [2580/5000], Loss: 0.4950\n",
      "Epoch [2590/5000], Loss: 0.4950\n",
      "Epoch [2600/5000], Loss: 0.4950\n",
      "Epoch [2610/5000], Loss: 0.4950\n",
      "Epoch [2620/5000], Loss: 0.4950\n",
      "Epoch [2630/5000], Loss: 0.4950\n",
      "Epoch [2640/5000], Loss: 0.4950\n",
      "Epoch [2650/5000], Loss: 0.4950\n",
      "Epoch [2660/5000], Loss: 0.4950\n",
      "Epoch [2670/5000], Loss: 0.4950\n",
      "Epoch [2680/5000], Loss: 0.4950\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2690/5000], Loss: 0.4950\n",
      "Epoch [2700/5000], Loss: 0.4950\n",
      "Epoch [2710/5000], Loss: 0.4950\n",
      "Epoch [2720/5000], Loss: 0.4950\n",
      "Epoch [2730/5000], Loss: 0.4950\n",
      "Epoch [2740/5000], Loss: 0.4950\n",
      "Epoch [2750/5000], Loss: 0.4950\n",
      "Epoch [2760/5000], Loss: 0.4950\n",
      "Epoch [2770/5000], Loss: 0.4950\n",
      "Epoch [2780/5000], Loss: 0.4950\n",
      "Epoch [2790/5000], Loss: 0.4950\n",
      "Epoch [2800/5000], Loss: 0.4950\n",
      "Epoch [2810/5000], Loss: 0.4950\n",
      "Epoch [2820/5000], Loss: 0.4950\n",
      "Epoch [2830/5000], Loss: 0.4950\n",
      "Epoch [2840/5000], Loss: 0.4950\n",
      "Epoch [2850/5000], Loss: 0.4950\n",
      "Epoch [2860/5000], Loss: 0.4950\n",
      "Epoch [2870/5000], Loss: 0.4950\n",
      "Epoch [2880/5000], Loss: 0.4950\n",
      "Epoch [2890/5000], Loss: 0.4950\n",
      "Epoch [2900/5000], Loss: 0.4950\n",
      "Epoch [2910/5000], Loss: 0.4950\n",
      "Epoch [2920/5000], Loss: 0.4950\n",
      "Epoch [2930/5000], Loss: 0.4950\n",
      "Epoch [2940/5000], Loss: 0.4950\n",
      "Epoch [2950/5000], Loss: 0.4950\n",
      "Epoch [2960/5000], Loss: 0.4950\n",
      "Epoch [2970/5000], Loss: 0.4950\n",
      "Epoch [2980/5000], Loss: 0.4950\n",
      "Epoch [2990/5000], Loss: 0.4950\n",
      "Epoch [3000/5000], Loss: 0.4950\n",
      "Epoch [3010/5000], Loss: 0.4950\n",
      "Epoch [3020/5000], Loss: 0.4950\n",
      "Epoch [3030/5000], Loss: 0.4950\n",
      "Epoch [3040/5000], Loss: 0.4950\n",
      "Epoch [3050/5000], Loss: 0.4950\n",
      "Epoch [3060/5000], Loss: 0.4950\n",
      "Epoch [3070/5000], Loss: 0.4950\n",
      "Epoch [3080/5000], Loss: 0.4950\n",
      "Epoch [3090/5000], Loss: 0.4950\n",
      "Epoch [3100/5000], Loss: 0.4950\n",
      "Epoch [3110/5000], Loss: 0.4950\n",
      "Epoch [3120/5000], Loss: 0.4950\n",
      "Epoch [3130/5000], Loss: 0.4950\n",
      "Epoch [3140/5000], Loss: 0.4950\n",
      "Epoch [3150/5000], Loss: 0.4950\n",
      "Epoch [3160/5000], Loss: 0.4950\n",
      "Epoch [3170/5000], Loss: 0.4950\n",
      "Epoch [3180/5000], Loss: 0.4950\n",
      "Epoch [3190/5000], Loss: 0.4950\n",
      "Epoch [3200/5000], Loss: 0.4950\n",
      "Epoch [3210/5000], Loss: 0.4950\n",
      "Epoch [3220/5000], Loss: 0.4950\n",
      "Epoch [3230/5000], Loss: 0.4950\n",
      "Epoch [3240/5000], Loss: 0.4950\n",
      "Epoch [3250/5000], Loss: 0.4950\n",
      "Epoch [3260/5000], Loss: 0.4950\n",
      "Epoch [3270/5000], Loss: 0.4950\n",
      "Epoch [3280/5000], Loss: 0.4950\n",
      "Epoch [3290/5000], Loss: 0.4950\n",
      "Epoch [3300/5000], Loss: 0.4950\n",
      "Epoch [3310/5000], Loss: 0.4950\n",
      "Epoch [3320/5000], Loss: 0.4950\n",
      "Epoch [3330/5000], Loss: 0.4950\n",
      "Epoch [3340/5000], Loss: 0.4950\n",
      "Epoch [3350/5000], Loss: 0.4950\n",
      "Epoch [3360/5000], Loss: 0.4950\n",
      "Epoch [3370/5000], Loss: 0.4950\n",
      "Epoch [3380/5000], Loss: 0.4950\n",
      "Epoch [3390/5000], Loss: 0.4950\n",
      "Epoch [3400/5000], Loss: 0.4950\n",
      "Epoch [3410/5000], Loss: 0.4950\n",
      "Epoch [3420/5000], Loss: 0.4950\n",
      "Epoch [3430/5000], Loss: 0.4950\n",
      "Epoch [3440/5000], Loss: 0.4950\n",
      "Epoch [3450/5000], Loss: 0.4950\n",
      "Epoch [3460/5000], Loss: 0.4950\n",
      "Epoch [3470/5000], Loss: 0.4950\n",
      "Epoch [3480/5000], Loss: 0.4950\n",
      "Epoch [3490/5000], Loss: 0.4950\n",
      "Epoch [3500/5000], Loss: 0.4950\n",
      "Epoch [3510/5000], Loss: 0.4950\n",
      "Epoch [3520/5000], Loss: 0.4950\n",
      "Epoch [3530/5000], Loss: 0.4950\n",
      "Epoch [3540/5000], Loss: 0.4950\n",
      "Epoch [3550/5000], Loss: 0.4950\n",
      "Epoch [3560/5000], Loss: 0.4950\n",
      "Epoch [3570/5000], Loss: 0.4950\n",
      "Epoch [3580/5000], Loss: 0.4950\n",
      "Epoch [3590/5000], Loss: 0.4950\n",
      "Epoch [3600/5000], Loss: 0.4950\n",
      "Epoch [3610/5000], Loss: 0.4950\n",
      "Epoch [3620/5000], Loss: 0.4950\n",
      "Epoch [3630/5000], Loss: 0.4950\n",
      "Epoch [3640/5000], Loss: 0.4950\n",
      "Epoch [3650/5000], Loss: 0.4950\n",
      "Epoch [3660/5000], Loss: 0.4950\n",
      "Epoch [3670/5000], Loss: 0.4950\n",
      "Epoch [3680/5000], Loss: 0.4950\n",
      "Epoch [3690/5000], Loss: 0.4950\n",
      "Epoch [3700/5000], Loss: 0.4950\n",
      "Epoch [3710/5000], Loss: 0.4950\n",
      "Epoch [3720/5000], Loss: 0.4950\n",
      "Epoch [3730/5000], Loss: 0.4950\n",
      "Epoch [3740/5000], Loss: 0.4950\n",
      "Epoch [3750/5000], Loss: 0.4950\n",
      "Epoch [3760/5000], Loss: 0.4950\n",
      "Epoch [3770/5000], Loss: 0.4950\n",
      "Epoch [3780/5000], Loss: 0.4950\n",
      "Epoch [3790/5000], Loss: 0.4950\n",
      "Epoch [3800/5000], Loss: 0.4950\n",
      "Epoch [3810/5000], Loss: 0.4950\n",
      "Epoch [3820/5000], Loss: 0.4950\n",
      "Epoch [3830/5000], Loss: 0.4950\n",
      "Epoch [3840/5000], Loss: 0.4950\n",
      "Epoch [3850/5000], Loss: 0.4950\n",
      "Epoch [3860/5000], Loss: 0.4950\n",
      "Epoch [3870/5000], Loss: 0.4950\n",
      "Epoch [3880/5000], Loss: 0.4950\n",
      "Epoch [3890/5000], Loss: 0.4950\n",
      "Epoch [3900/5000], Loss: 0.4950\n",
      "Epoch [3910/5000], Loss: 0.4950\n",
      "Epoch [3920/5000], Loss: 0.4950\n",
      "Epoch [3930/5000], Loss: 0.4950\n",
      "Epoch [3940/5000], Loss: 0.4950\n",
      "Epoch [3950/5000], Loss: 0.4950\n",
      "Epoch [3960/5000], Loss: 0.4950\n",
      "Epoch [3970/5000], Loss: 0.4950\n",
      "Epoch [3980/5000], Loss: 0.4950\n",
      "Epoch [3990/5000], Loss: 0.4950\n",
      "Epoch [4000/5000], Loss: 0.4950\n",
      "Epoch [4010/5000], Loss: 0.4950\n",
      "Epoch [4020/5000], Loss: 0.4950\n",
      "Epoch [4030/5000], Loss: 0.4950\n",
      "Epoch [4040/5000], Loss: 0.4950\n",
      "Epoch [4050/5000], Loss: 0.4950\n",
      "Epoch [4060/5000], Loss: 0.4950\n",
      "Epoch [4070/5000], Loss: 0.4950\n",
      "Epoch [4080/5000], Loss: 0.4950\n",
      "Epoch [4090/5000], Loss: 0.4950\n",
      "Epoch [4100/5000], Loss: 0.4950\n",
      "Epoch [4110/5000], Loss: 0.4950\n",
      "Epoch [4120/5000], Loss: 0.4950\n",
      "Epoch [4130/5000], Loss: 0.4950\n",
      "Epoch [4140/5000], Loss: 0.4950\n",
      "Epoch [4150/5000], Loss: 0.4950\n",
      "Epoch [4160/5000], Loss: 0.4950\n",
      "Epoch [4170/5000], Loss: 0.4950\n",
      "Epoch [4180/5000], Loss: 0.4950\n",
      "Epoch [4190/5000], Loss: 0.4950\n",
      "Epoch [4200/5000], Loss: 0.4950\n",
      "Epoch [4210/5000], Loss: 0.4950\n",
      "Epoch [4220/5000], Loss: 0.4950\n",
      "Epoch [4230/5000], Loss: 0.4950\n",
      "Epoch [4240/5000], Loss: 0.4950\n",
      "Epoch [4250/5000], Loss: 0.4950\n",
      "Epoch [4260/5000], Loss: 0.4950\n",
      "Epoch [4270/5000], Loss: 0.4950\n",
      "Epoch [4280/5000], Loss: 0.4950\n",
      "Epoch [4290/5000], Loss: 0.4950\n",
      "Epoch [4300/5000], Loss: 0.4950\n",
      "Epoch [4310/5000], Loss: 0.4950\n",
      "Epoch [4320/5000], Loss: 0.4950\n",
      "Epoch [4330/5000], Loss: 0.4950\n",
      "Epoch [4340/5000], Loss: 0.4950\n",
      "Epoch [4350/5000], Loss: 0.4950\n",
      "Epoch [4360/5000], Loss: 0.4950\n",
      "Epoch [4370/5000], Loss: 0.4950\n",
      "Epoch [4380/5000], Loss: 0.4950\n",
      "Epoch [4390/5000], Loss: 0.4950\n",
      "Epoch [4400/5000], Loss: 0.4950\n",
      "Epoch [4410/5000], Loss: 0.4950\n",
      "Epoch [4420/5000], Loss: 0.4950\n",
      "Epoch [4430/5000], Loss: 0.4950\n",
      "Epoch [4440/5000], Loss: 0.4950\n",
      "Epoch [4450/5000], Loss: 0.4950\n",
      "Epoch [4460/5000], Loss: 0.4950\n",
      "Epoch [4470/5000], Loss: 0.4950\n",
      "Epoch [4480/5000], Loss: 0.4950\n",
      "Epoch [4490/5000], Loss: 0.4950\n",
      "Epoch [4500/5000], Loss: 0.4950\n",
      "Epoch [4510/5000], Loss: 0.4950\n",
      "Epoch [4520/5000], Loss: 0.4950\n",
      "Epoch [4530/5000], Loss: 0.4950\n",
      "Epoch [4540/5000], Loss: 0.4950\n",
      "Epoch [4550/5000], Loss: 0.4950\n",
      "Epoch [4560/5000], Loss: 0.4950\n",
      "Epoch [4570/5000], Loss: 0.4950\n",
      "Epoch [4580/5000], Loss: 0.4950\n",
      "Epoch [4590/5000], Loss: 0.4950\n",
      "Epoch [4600/5000], Loss: 0.4950\n",
      "Epoch [4610/5000], Loss: 0.4950\n",
      "Epoch [4620/5000], Loss: 0.4950\n",
      "Epoch [4630/5000], Loss: 0.4950\n",
      "Epoch [4640/5000], Loss: 0.4950\n",
      "Epoch [4650/5000], Loss: 0.4950\n",
      "Epoch [4660/5000], Loss: 0.4950\n",
      "Epoch [4670/5000], Loss: 0.4950\n",
      "Epoch [4680/5000], Loss: 0.4950\n",
      "Epoch [4690/5000], Loss: 0.4950\n",
      "Epoch [4700/5000], Loss: 0.4950\n",
      "Epoch [4710/5000], Loss: 0.4950\n",
      "Epoch [4720/5000], Loss: 0.4950\n",
      "Epoch [4730/5000], Loss: 0.4950\n",
      "Epoch [4740/5000], Loss: 0.4950\n",
      "Epoch [4750/5000], Loss: 0.4950\n",
      "Epoch [4760/5000], Loss: 0.4950\n",
      "Epoch [4770/5000], Loss: 0.4950\n",
      "Epoch [4780/5000], Loss: 0.4950\n",
      "Epoch [4790/5000], Loss: 0.4950\n",
      "Epoch [4800/5000], Loss: 0.4950\n",
      "Epoch [4810/5000], Loss: 0.4950\n",
      "Epoch [4820/5000], Loss: 0.4950\n",
      "Epoch [4830/5000], Loss: 0.4950\n",
      "Epoch [4840/5000], Loss: 0.4950\n",
      "Epoch [4850/5000], Loss: 0.4950\n",
      "Epoch [4860/5000], Loss: 0.4950\n",
      "Epoch [4870/5000], Loss: 0.4950\n",
      "Epoch [4880/5000], Loss: 0.4950\n",
      "Epoch [4890/5000], Loss: 0.4950\n",
      "Epoch [4900/5000], Loss: 0.4950\n",
      "Epoch [4910/5000], Loss: 0.4950\n",
      "Epoch [4920/5000], Loss: 0.4950\n",
      "Epoch [4930/5000], Loss: 0.4950\n",
      "Epoch [4940/5000], Loss: 0.4950\n",
      "Epoch [4950/5000], Loss: 0.4950\n",
      "Epoch [4960/5000], Loss: 0.4950\n",
      "Epoch [4970/5000], Loss: 0.4950\n",
      "Epoch [4980/5000], Loss: 0.4950\n",
      "Epoch [4990/5000], Loss: 0.4950\n",
      "Epoch [5000/5000], Loss: 0.4950\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate training data\n",
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Scale the input data\n",
    "x_scaled = (x - np.pi) /  10\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "x_tensor = torch.tensor(x_scaled.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "y_tensor = torch.tensor(y.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "\n",
    "# Define the RNN model\n",
    "class RNNSin(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(RNNSin, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "        \n",
    "    def forward(self, input):\n",
    "        batch_size = input.size(0)\n",
    "        hidden = self.init_hidden(batch_size)\n",
    "        output, _ = self.rnn(input, hidden)\n",
    "        output = self.fc(output[:, -1, :])\n",
    "        return output\n",
    "    \n",
    "    def init_hidden(self, batch_size):\n",
    "        hidden = torch.zeros(1, batch_size, self.hidden_size)\n",
    "        return hidden\n",
    "\n",
    "# Set hyperparameters\n",
    "input_size = 1\n",
    "hidden_size = 200  # Increased hidden size\n",
    "output_size = 1\n",
    "learning_rate = 0.001  # Adjusted learning rate\n",
    "num_epochs = 5000\n",
    "\n",
    "# Initialize the RNN model\n",
    "model = RNNSin(input_size, hidden_size, output_size)\n",
    "\n",
    "# Loss function and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=learning_rate)  # Changed optimizer to SGD\n",
    "\n",
    "# Training loop\n",
    "losses = []\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass\n",
    "    output = model(x_tensor)\n",
    "    loss = criterion(output, y_tensor)\n",
    "    \n",
    "    # Backward pass and optimization\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    # Print progress\n",
    "    if (epoch+1) % 10 == 0:\n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "    \n",
    "    # Store the loss\n",
    "    losses.append(loss.item())\n",
    "\n",
    "# Plot the training loss\n",
    "plt.plot(losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss')\n",
    "plt.show()\n",
    "\n",
    "# Test the model\n",
    "x_test = np.linspace(0, 2*np.pi, 200)\n",
    "x_test_scaled = (x_test - np.pi) / 10\n",
    "x_test_tensor = torch.tensor(x_test_scaled.reshape(-1, 1, 1), dtype=torch.float32)\n",
    "with torch.no_grad():\n",
    "    y_pred = model(x_test_tensor)\n",
    "\n",
    "# Plot the true sin(x) and predicted sin(x)\n",
    "plt.plot(x, y, label='True sin(x)')\n",
    "plt.plot(x_test, y_pred.squeeze().numpy(), label='Predicted sin(x)')\n",
    "plt.legend()\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "plt.title('sin(x) Approximation')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1425712",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 loss: 0.0004100106889382005 test loss: 0.00023189325293060392\n",
      "Epoch: 1 loss: 2.8032579848513706e-06 test loss: 1.7793232700569206e-06\n",
      "Epoch: 2 loss: 4.95620270157815e-0776 test loss: 4.778641482516832e-07\n",
      "Epoch: 3 loss: 3.148060727653501e-077 test loss: 3.102063033111335e-07\n",
      "Epoch: 4 loss: 3.051802366371703e-077 test loss: 3.051802366371703e-07\n",
      "Epoch: 5 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 6 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 7 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 8 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 9 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 10 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 11 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 12 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 13 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n",
      "Epoch: 14 loss: 3.0474313916784013e-07 test loss: 3.047454697480134e-07\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "\n",
    "SEED = 1\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "\n",
    "### Property of Sin function\n",
    "\n",
    "\n",
    "T = 20 # Period of Sin Function\n",
    "L = 1000 # Sample Lenght of Sin function\n",
    "N = 200   # Sequence Length\n",
    "\n",
    "\n",
    "x = np.empty((N, L), 'int32')\n",
    "x[:] = np.array(range(L))\n",
    "data = np.sin(x * (1.0 / T)).astype('float32')\n",
    "x_f = np.array(range(1500))\n",
    "data_f = np.sin(x_f * (1.0 / T)).astype('float32')\n",
    "\n",
    "train_data = torch.from_numpy(data[1:, :-1])\n",
    "train_target = torch.from_numpy(data[1:, 1:])\n",
    "test_data = torch.from_numpy(data[:1, :-1])\n",
    "test_target = torch.from_numpy(data[:1, 1:])\n",
    "\n",
    "\n",
    "\n",
    "class RNNSin(nn.Module):\n",
    "    def __init__(self, in_dim, h_dim, out_dim):\n",
    "        super(RNNSin, self).__init__()\n",
    "        self.rnn = nn.RNNCell(in_dim, h_dim)\n",
    "        self.linear = nn.Linear(h_dim, out_dim)\n",
    "        self.h_dim = h_dim\n",
    "\n",
    "    def forward(self, train_data, future = 0):\n",
    "        outputs = []        \n",
    "        h_t = torch.zeros((train_data.size(0),self.h_dim))\n",
    "        for i, input_t in enumerate(train_data.chunk(train_data.size(1), dim=1)):\n",
    "            h_t = self.rnn(input_t,h_t)\n",
    "            output = self.linear(h_t)\n",
    "            outputs.append(output)\n",
    "        for i in range(future):            \n",
    "            h_t = self.rnn(outputs[-1],h_t)\n",
    "            output = self.linear(h_t)\n",
    "            outputs += [output]\n",
    "        outputs = torch.stack(outputs, 1).squeeze(2)\n",
    "        return outputs\n",
    "\n",
    "    \n",
    "seq = RNNSin(1,200,1)\n",
    "loss_fn = nn.MSELoss()\n",
    "optimizer = optim.LBFGS(seq.parameters(), lr=0.4)\n",
    "Nepochs = 15\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(30,15))\n",
    "image_list = []\n",
    "for it in range(Nepochs):\n",
    "    \n",
    "    \n",
    "    def closure():\n",
    "        optimizer.zero_grad()\n",
    "        out = seq(train_data)\n",
    "        loss = loss_fn(out, train_target)\n",
    "        print('\\rEpoch:', it,'loss:', loss.item(), end='')\n",
    "        loss.backward()\n",
    "        return loss\n",
    "    optimizer.step(closure)\n",
    "    \n",
    "    future = 500\n",
    "    # begin to predict, no need to track gradient here\n",
    "    with torch.no_grad():\n",
    "        pred = seq(test_data, future=future)\n",
    "        loss = loss_fn(pred[:, :-future], test_target)\n",
    "        print(' test loss:', loss.item())\n",
    "        y = pred.detach().numpy()\n",
    "    plt.cla()\n",
    "    ax.set_xlabel('x', fontsize=26)\n",
    "    ax.set_ylabel('y', fontsize=26)\n",
    "    ax.set_xlim([0,1600])\n",
    "    ax.set_ylim([-1.5, 1.5])\n",
    "    ax.plot(x_f, data_f, \"-r\", linewidth = 3.0, label=\"Actual Series\")\n",
    "    ax.plot(np.arange(train_data.size(1)), y[0][:train_data.size(1)], '--k', linewidth = 2.0, label=\"Training\")\n",
    "    ax.plot(np.arange(train_data.size(1), train_data.size(1) + future), y[0][train_data.size(1):], 'b' + ':', linewidth = 3.0, label=\"Test + Future\")\n",
    "    ax.vlines(1000, -1.5, 1.5, colors=\"black\", linestyles='solid', linewidth=2.0)   \n",
    "    ax.text(500.0, 1.4, 'It = %d,' %it, fontdict={'size': 26, 'color':  'black'})\n",
    "    plt.legend(loc=2, prop={'size': 20})\n",
    "    fig.canvas.draw()\n",
    "    image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')\n",
    "    image  = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
    "    image_list.append(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5058c911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([199, 999])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = seq(train_data)\n",
    "m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42df1a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([199, 999])\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a7096bb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 999])\n"
     ]
    }
   ],
   "source": [
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2fc25dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([199, 999])\n"
     ]
    }
   ],
   "source": [
    "print(train_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "287978d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 999])\n"
     ]
    }
   ],
   "source": [
    "print(test_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f39f809b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 1000)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94d25ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.size(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f24af654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(999,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0][:train_data.size(1)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "56e3366b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1499,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1920e435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RNNSin(\n",
       "  (rnn): RNNCell(1, 200)\n",
       "  (linear): Linear(in_features=200, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "58f78f7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 loss: 0.0017511987825855613 test loss: 0.0016670176992192864\n",
      "Epoch: 1 loss: 0.0005029616877436638 test loss: 0.00047140821698121727\n",
      "Epoch: 2 loss: 5.547151522478089e-055 test loss: 5.393323226599023e-05\n",
      "Epoch: 3 loss: 1.2755008356180042e-05 test loss: 1.1619599717960227e-05\n",
      "Epoch: 4 loss: 2.75086858891882e-0666 test loss: 2.6940351744997315e-06\n",
      "Epoch: 5 loss: 5.997751486574998e-076 test loss: 5.863431056241097e-07\n",
      "Epoch: 6 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n",
      "Epoch: 7 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n",
      "Epoch: 8 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n",
      "Epoch: 9 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n",
      "Epoch: 10 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n",
      "Epoch: 11 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n",
      "Epoch: 12 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n",
      "Epoch: 13 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n",
      "Epoch: 14 loss: 2.6503914796194294e-07 test loss: 2.650358226219396e-07\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "SEED = 1\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "\n",
    "\n",
    "### Property of Sin function\n",
    "\n",
    "T = 20  # Period of Sin Function\n",
    "L = 1000  # Sample Length of Sin function\n",
    "N = 200  # Sequence Length\n",
    "\n",
    "x = np.empty((N, L), 'int32')\n",
    "x[:] = np.array(range(L))\n",
    "data = np.sin(x * (1.0 / T)).astype('float32')\n",
    "x_f = np.array(range(1500))\n",
    "data_f = np.sin(x_f * (1.0 / T)).astype('float32')\n",
    "\n",
    "train_data = torch.from_numpy(data[1:, :-1])\n",
    "train_target = torch.from_numpy(data[1:, 1:])\n",
    "test_data = torch.from_numpy(data[:1, :-1])\n",
    "test_target = torch.from_numpy(data[:1, 1:])\n",
    "\n",
    "\n",
    "class RNNCell(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size):\n",
    "        super(RNNCell, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.weight_ih = nn.Parameter(torch.Tensor(hidden_size, input_size))\n",
    "        self.weight_hh = nn.Parameter(torch.Tensor(hidden_size, hidden_size))\n",
    "        self.bias_ih = nn.Parameter(torch.Tensor(hidden_size))\n",
    "        self.bias_hh = nn.Parameter(torch.Tensor(hidden_size))\n",
    "        self.reset_parameters()\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        nn.init.kaiming_uniform_(self.weight_ih, a=np.sqrt(5))\n",
    "        nn.init.kaiming_uniform_(self.weight_hh, a=np.sqrt(5))\n",
    "        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_ih)\n",
    "        bound = 1 / np.sqrt(fan_in)\n",
    "        nn.init.uniform_(self.bias_ih, -bound, bound)\n",
    "        nn.init.uniform_(self.bias_hh, -bound, bound)\n",
    "\n",
    "    def forward(self, input, hx):\n",
    "        gate = torch.matmul(input, self.weight_ih.t()) + self.bias_ih + torch.matmul(hx, self.weight_hh.t()) + self.bias_hh\n",
    "        hx = torch.tanh(gate)\n",
    "        return hx\n",
    "\n",
    "\n",
    "class RNNSin(nn.Module):\n",
    "    def __init__(self, in_dim, h_dim, out_dim):\n",
    "        super(RNNSin, self).__init__()\n",
    "        self.rnn = RNNCell(in_dim, h_dim)\n",
    "        self.linear = nn.Linear(h_dim, out_dim)\n",
    "        self.h_dim = h_dim\n",
    "\n",
    "    def forward(self, train_data, future=0):\n",
    "        outputs = []\n",
    "        h_t = torch.zeros((train_data.size(0), self.h_dim))\n",
    "        for i, input_t in enumerate(train_data.chunk(train_data.size(1), dim=1)):\n",
    "            h_t = self.rnn(input_t, h_t)\n",
    "            output = self.linear(h_t)\n",
    "            outputs.append(output)\n",
    "        for i in range(future):\n",
    "            h_t = self.rnn(outputs[-1], h_t)\n",
    "            output = self.linear(h_t)\n",
    "            outputs += [output]\n",
    "        outputs = torch.stack(outputs, 1).squeeze(2)\n",
    "        return outputs\n",
    "\n",
    "\n",
    "seq = RNNSin(1, 200, 1)\n",
    "loss_fn = nn.MSELoss()\n",
    "optimizer = optim.LBFGS(seq.parameters(), lr=0.4)\n",
    "Nepochs = 15\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(30, 15))\n",
    "image_list = []\n",
    "for it in range(Nepochs):\n",
    "    def closure():\n",
    "        optimizer.zero_grad()\n",
    "        out = seq(train_data)\n",
    "        loss = loss_fn(out, train_target)\n",
    "        print('\\rEpoch:', it, 'loss:', loss.item(), end='')\n",
    "        loss.backward()\n",
    "        return loss\n",
    "\n",
    "    optimizer.step(closure)\n",
    "\n",
    "    future = 500\n",
    "    # begin to predict, no need to track gradient here\n",
    "    with torch.no_grad():\n",
    "        pred = seq(test_data, future=future)\n",
    "        loss = loss_fn(pred[:, :-future], test_target)\n",
    "        print(' test loss:', loss.item())\n",
    "        y = pred.detach().numpy()\n",
    "    plt.cla()\n",
    "    ax.set_xlabel('x', fontsize=26)\n",
    "    ax.set_ylabel('y', fontsize=26)\n",
    "    ax.set_xlim([0, 1600])\n",
    "    ax.set_ylim([-1.5, 1.5])\n",
    "    ax.plot(x_f, data_f, \"-r\", linewidth=3.0, label=\"Actual Series\")\n",
    "    ax.plot(np.arange(train_data.size(1)), y[0][:train_data.size(1)], '--k', linewidth=2.0, label=\"Training\")\n",
    "    ax.plot(\n",
    "        np.arange(train_data.size(1), train_data.size(1) + future),\n",
    "        y[0][train_data.size(1):],\n",
    "        'b' + ':',\n",
    "        linewidth=3.0,\n",
    "        label=\"Test + Future\"\n",
    "    )\n",
    "    ax.vlines(1000, -1.5, 1.5, colors=\"black\", linestyles='solid', linewidth=2.0)\n",
    "    ax.text(500.0, 1.4, 'It = %d,' % it, fontdict={'size': 26, 'color': 'black'})\n",
    "    plt.legend(loc=2, prop={'size': 20})\n",
    "    fig.canvas.draw()\n",
    "    image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')\n",
    "    image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
    "    image_list.append(image)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3397131e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
